Hydrogel-based wearable electrochemical biosensors (HWEBs) are emerging biomedical devices that have recently received immense interest. The exceptional properties of HWEBs include excellent biocompatibility with hydrophilic nature, high porosity, tailorable permeability, the capability of reliable and accurate detection of disease biomarkers, suitable device-human interface, facile adjustability, and stimuli-responsive to the nanofiller materials. Although the biomimetic three-dimensional hydrogels can immobilize bioreceptors, such as enzymes and aptamers, without any loss in their activities. However, most HWEBs suffer from low mechanical strength and electrical conductivity. Many studies have been performed on emerging electroactive nanofillers, including biomacromolecules, carbon-based materials, and inorganic and organic nanomaterials, to tackle these issues. Non-conductive hydrogels and even conductive hydrogels may be modified by nanofillers as well as redox species. All these modifications have led to the design and development of efficient nanocomposites as electrochemical biosensors. In this review, both conductive-based and non-conductive-based hydrogels derived from natural and synthetic polymers are systematically reviewed. The main synthesis methods and characterization techniques are addressed. The mechanical properties and electrochemical behavior of HWEBs are discussed in detail. Finally, the prospects and potential applications of HWEBs in biosensing, healthcare monitoring, and clinical diagnostics are highlighted.

Three-dimensional (3D) printing technology, also known as additive manufacturing (AM), has emerged as an attractive state-of-the-art tool for precisely fabricating functional materials with complex geometries, championing several advancements in tissue engineering, regenerative medicine, and therapeutics. However, this technology has an untapped potential for biotechnological applications, such as sensor and biosensor development. By exploring these avenues, the scope of 3D printing technology can be expanded and pave the way for groundbreaking innovations in the biotechnology field. Indeed, new printing materials and printers would offer new possibilities for seamlessly incorporating biological functionalities within the growing 3D scaffolds. Herein, we review the extrusion-based 3D printing modalities emphasizing biosensor applications. We highlighted the application of natural, synthetic, and composite biomaterials as 3D-printed soft hydrogels. Emphasis was placed on the approach by which the sensing molecules were introduced during the fabrication process. Finally, the future perspective was provided.

Wearable Biosensor Technology (WBT) has emerged as a transformative tool in the educational system over the past decade. This systematic review encompasses a comprehensive analysis of WBT utilization in educational settings over a 10-year span (2012-2022), highlighting both the evolution of this field and its integration to address challenges in education by integrating technology to solve specific educational challenges, such as enhancing student engagement, monitoring stress and cognitive load, improving learning experiences, and providing real-time feedback for both students and educators. By exploring these aspects, the review sheds light on the potential implications of WBT for the future of learning. A rigorous and systematic search of major academic databases, including Google Scholar and Scopus, was conducted in accordance with PRISMA guidelines. Relevant studies were selected based on predefined inclusion and exclusion criteria. The articles selected were assessed for methodological quality and bias using established tools. The process of data extraction and synthesis followed a structured framework. Key findings include the shift from theoretical exploration to practical implementation with the EEG being the predominant measurement, aiming to explore mental states, physiological constructs, and teaching effectiveness. Wearable biosensors are significantly impacting the educational field, serving as an important resource for educators and a tool for students. Their application has the potential to transform and optimize academic practices through sensors that capture biometric data, enabling the implementation of metrics and models to understand the development and performance of students and professors in an academic environment, as well as to gain insights into the learning process.

Understanding user challenges with light dosimeters is crucial for designing more acceptable devices and advancing light exposure research. We systematically evaluated the usability and acceptability of the lido (light dosimeter) by 29 participants who wore the dosimeter near the corneal plane of the eye for 5 days. Common reasons for not wearing the dosimeter included exercise, recharging, wet environments, public settings, and discomfort. Despite these issues, participants adhered to using the dosimeter, with high compliance (89% of recording time). Our findings reveal a significant discrepancy between mean melanopic Equivalent Daylight Illuminance (300 lxmEDI) and median values (51 lxmEDI). This discrepancy indicates that, for most of the time, participants experienced significantly lower light levels. Specifically, participants were exposed to light levels above 250 lxmEDI for only 14% of their wearing time. This highlights the need for increased exposure to recommended light levels. In the evening, participants' light exposure was below the recommended 10 lxmEDI for 58% of the wear time, which is in line with the guidelines for reducing light exposure before sleep. This study highlights the critical need for strategies to enhance daily light exposure, aligning better with circadian health recommendations.

Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating Photoplethysmogram (PPG) and Electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls. Feature selection techniques were used to systematically select key features which were identified for their clinical relevance and significance in capturing cardiovascular dynamics and to reduce computational complexity and to decrease the chance of overfitting the ML algorithms. These features are then utilized to train and evaluate machine learning algorithms, resulting in a model with an impressive accuracy of 98%, sensitivity of 97.60%, specificity of 96.90%, and precision of 97.20%. The integrated approach outperforms single-signal strategies, showcas-ng its potential for early, precise, and non-invasive heart failure diagnosis. Furthermore, the study underscores the significance of continuous monitoring through wearables, emphasizing the benefits of integrating multiple signals for a comprehensive evaluation of cardiovascular health. The proposed approach holds promise for implementation in hardware systems to enable continuous monitoring, aiding in early detection and prevention of critical health conditions.

Biosensors are smart devices that convert biochemical responses to electrical signals. Designing biosensor devices with high sensitivity and selectivity is of great interest because of their wide range of functional operations. However, the major obstacles in the practical application of biosensors are their binding affinity toward biomolecules and the conversion and amplification of the interaction to various signals such as electrical, optical, gravimetric, and electrochemical signals. Additionally, enhancement of sensitivity, limit of detection, time of response, reproducibility, and stability are considerable challenges when designing an efficient biosensor. In this regard, hybrid composites have high sensitivity, selectivity, thermal stability, and tunable electrical conductivities. The integration of phthalocyanines (Pcs) with conductive materials such as carbon nanomaterials or metal nanoparticles (MNPs) improves the electrochemical response, signal amplification, and stability of biosensors. This review explores recent advancements in hybrid Pcs for biomolecule detection. Herein, we discuss the synthetic strategies, material properties, working mechanisms, and integration methods for designing electrochemical biosensors. Finally, the challenges and future directions of hybrid Pc composites for biosensor applications are discussed.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, entailing several motor-related symptoms that contribute to a reduced quality of life in affected subjects. Recent advances in wearable technologies and computing resources have shown great potential for the assessment of PD-related symptoms. However, the potential applications (e.g., early diagnosis, prognosis and monitoring) and key features of digital biomarkers for motor symptoms of PD (DB-MS-PD) have not been comprehensively studied. This study aims to provide a state-of-the-art review of current digital biomarker definitions for PD, focusing on the use of wearable devices. This review systematically examines research articles from 2012 to 2024, focusing on key features and recent technologies in PD research. A total of 22 studies were included and thoroughly analyzed. Results indicate that DB-MS-PD can accurately distinguish patients with PD (PwPD) from healthy controls (HC), assess disease severity or treatment response, and detect motor symptoms. Large sample sizes, proper validation, non-invasive devices, and ecological monitoring make DB-MS-PD promising for improving PD management. Challenges include sample and method heterogeneity and lack of public datasets. Future studies can leverage evidence of the current literature to provide more effective and ready-to-use digital tools for monitoring PD.

Reminding the courier through wearable artificial intelligence devices to prevent accidents is not only beneficial to the courier's safety but also will save a lot of insurance premiums for express companies, therefore, it is worth investigating what factors can influence the acceptance of wearable artificial intelligence devices by couriers. Push-pull-mooring (PPM) theory and affective event theory (AET) are integrated to test couriers’ adoption to wearable safety detection devices. Social influence, perceived security, personal innovativeness and affective event reaction are applied to research model. Questionnaires are distributed among several listed express companies and 263 valid questionnaires are used for empirical test. Empirical results indicated that social influence, perceived safety, personal innovativeness and affective event reaction are positively related to usage with coefficients 0.218, 0.301, 0.698 and 0.309. Personal innovativeness has positively moderating effects on relationships between affective event reaction, perceived security and usage with coefficients 0.145 and 0.106, whereas has no significant moderating effect on relationship between social influence and usage. The research aims to help support the proliferation and adoption of wearable artificial intelligence devices to optimize the current state of the express industry and improve the interaction between couriers and managers, creating an active management strategy that will allow express companies to thrive. This study can help express companies reduce insurance costs and therefore reduce the premium pricing of insurance companies for the courier industry.. This study also provides design suggestions for the application of wearable artificial intelligence devices based on chat GPT in various working conditions.

The exploitation of lipid membranes in biosensors has provided the ability to reconstitute a considerable part of their functionality to detect trace of food toxicants and environmental pollutants. Nanotechnology enabled sensor miniaturization and extended the range of biological moieties that could be immobilized within a lipid bilayer device. This chapter reviews recent progress in biosensor technologies based on lipid membranes suitable for environmental applications and food quality monitoring. Numerous biosensing applications are presented, putting emphasis on novel systems, new sensing techniques and nanotechnology-based transduction schemes. The range of analytes that can be currently detected include, insecticides, pesticides, herbicides, metals, toxins, antibiotics, microorganisms, hormones, dioxins, etc. Technology limitations and future prospects are discussed, focused on the evaluation/ validation and eventually commercialization of the proposed sensors.

The exploitation of lipid membranes in biosensors has provided the ability to reconstitute a considerable part of their functionality to detect trace of food toxicants and environmental pollutants. Nanotechnology enabled sensor miniaturization and extended the range of biological moieties that could be immobilized within a lipid bilayer device. This chapter reviews recent progress in biosensor technologies based on lipid membranes suitable for environmental applications and food quality monitoring. Numerous biosensing applications are presented, putting emphasis on novel systems, new sensing techniques and nanotechnology-based transduction schemes. The range of analytes that can be currently detected include, insecticides, pesticides, herbicides, metals, toxins, antibiotics, microorganisms, hormones, dioxins, etc. Technology limitations and future prospects are discussed, focused on the evaluation/ validation and eventually commercialization of the proposed sensors.

This article uses machine learning analysis of wearable sensor data to explore the topic of driver differentiation. The study focuses on using detailed analyzes of data collected from wearable sensors to identify patterns that differentiate skilled drivers from inexperienced ones. On a predetermined driving route, participants experienced a variety of driving conditions, including parking, navigating cities, driving on highways and driving through residential neighborhoods. The gathered data was analyzed using machine learning techniques, yielding a classification accuracy of 94% overall. The results highlight important differences in sensor data between inexperienced and experienced drivers, providing insight into possible uses for improving safety protocols, driver education, and personalized feedback systems. The article fosters improvements in traffic safety and driver education by offering insightful information on the link between driver behavior analysis and machine learning.

In this article, we describe a simple procedure for evaluating the sterility of nutrient media using a shear-horizontal surface acoustic wave (SH-SAW) biosensor. We used the SH-SAW biosensor platform to monitor the time-dependent phase responses of the material for the media that binds to a monolayer of 3-mercaptopropanyl-N-hydroxy succinimide on the biosensor surface. Increases in the phase shift less than 50% of the starting phase shift recorded 180 minutes after the sterilization indicate a successful sterility test of the media. Our devices were then applied to sterility testing using a viable Escherichia coli (E. coli) K-12 solution to confirm our hypothesis. Filtered LB Broth was the negative control, and a 24-hour culture of E. coli was the positive control. Before testing, the cell solution was pelleted, the media decanted, and then replaced with a 1x phosphate-buffered saline solution (1x PBS). Since we know that the N-hydroxysuccinimide residues will bind to amine groups, only in cases where microbial species are present will the surface binding increase greater than 50%. We confirmed our hypothesis using SH-SAW biosensors and overnight E. coli cultures. The sensitivity for our 250 MHz quartz SH-SAW sensors was calculated based on the response from the E. coli K-12 solution over the linear dynamic range, which appears to be 2x10^4 – 2x10^7 CFU per ml and has a sensitivity of S = 1.655x10^5 o m2 Kg-1. The limit of detection (LOD) for these sensors was calculated to be approximately 2x10^-9 grams (g), which is estimated to be approximately 2,000 Escherichia coli (E. coli) K-12 cells. However, the sensitivity and insertion loss remained approximately unchanged before and after coating the sensor with 3,3'-Dithiobis(succinimidyl) Propionate (DSP).

Solid tumors universally possess the ability to evade contact inhibition of proliferation (CIP), a mechanism halting cell proliferation when cell-cell contacts occur. Merlin, an ERM-like protein, crucially regulates CIP and is frequently deactivated in various cancers, indicating its signifi-cance as a tumor suppressor in cancer biology. Despite extensive investigations into Merlin's role in cancer, its lack of intrinsic catalytic activity and frequent conformation changes has made it notoriously challenging to study. To address this challenge, we harnessed innovative luciferase technologies to create and validate a split-luciferase biosensor system. This system enables precise quantification of Merlin's conformation and activity both in vitro and within living cells. This biosensor significantly enhances the study of Merlin's molecular functions, serving as a potent tool to explore its contributions to CIP and tumorigenesis.

As cardiovascular diseases continue to be a leading cause of mortality, recent-ly, wearable devices for monitoring cardiac activity have gained much interest among medical community. This paper introduces an innovative ECG monitoring system based on a single – lead ECG machine enhanced with machine learning methods. The system only processes and analyses the ECG data, but also predict potential heart disease at an early stage. The wearable device was built on the ADS1298 and a microcontroller STM32L151xD. A server module based on REST API architecture style was designed to fa-cilitate interaction with web-based segment of the system. The module is responsible for receiving data in real time from microcontroller and their deliver to web – based segment. Algorithms for analyzing ECG signals have been developed, including band filter artifact removal, K-means clustering for signal segmentation, and PQRST analysis. Machine leaning methods as Isolation Forest have been employed for ECG anomaly detection. Moreover, a comparative analysis with various machine learning methods, including lo-gistic regression, random forest, SVM, XGBoost, decision forest and, CNNs was conducted to predict cardiovascular diseases. Convoluted Neural Networks (CNN) showed an accu-racy of 0.926, proving high effectiveness in ECG data process.

Biosensors are essential tools which have been traditionally used to monitor environmental pollution, detect the presence of toxic elements and biohazardous bacteria or virus in organic matter and biomolecules for clinical diagnostics. In the last couple of decades, the scientific community has witnessed their widespread application in the fields of military, health care, industrial process control, environmental monitoring, food-quality control, and microbiology. Biosensor technology has greatly evolved from the in vitro studies based on the biosensing ability of organic beings to the highly sophisticated world of nanofabrication enabled miniaturized biosensors. The incorporation of nanotechnology in the vast field of biosensing has led to the development of novel sensors and sensing mechanisms, as well as an increase in the sensitivity and performance of the existing biosensors. Additionally, the nanoscale dimension further assists the development of sensors for rapid and simple detection in vivo as well as the ability to probe single-biomolecules and obtain critical information for their detection and analysis. However, the major drawbacks of this include, but are not limited to potential toxicities associated with the unavoidable release of nanoparticles into the environment, miniaturization induced unreliability, lack of automation, and difficulty of integrating the nanostructured-based biosensors as well as unreliable transduction signals from these devices. Although the field of biosensors is vast, we intend to explore various nanotechnology enabled biosensors as part of this review article and provide a brief description of their fundamental working principles and potential applications.

The present Feature Paper highlights the application of bacterial surface (S-) layer proteins as versatile components for the fabrication of biosensors. One technologically relevant feature of S‑layer proteins is their ability to self-assemble on many surfaces and interfaces to form a crystalline 2D protein lattice. The S-layer lattice on the surface of a biosensor becomes part of the interface architecture, linking the bioreceptor to the transducer interface, which may cause signal amplification. The S-layer lattice as ultrathin, highly porous structure with functional groups in a well-defined special distribution and orientation and an overall anti-fouling characteristics can significantly raise the limit in terms of variety and ease of bioreceptor immobilization, compactness of bioreceptor molecule arrangement, sensitivity, specificity, and detection limit for many types of biosensors. The present paper discusses and summarizes examples for the successful implementation of S-layer lattices on biosensor surfaces in order to give a comprehensive overview on the application potential of these bioinspired S-layer protein-based biosensors.

Safety and quality are key issues for the food industry. Consequently, there is a growing demand to preserve the food chain and products against substances toxic, harmful to human health such as contaminants, allergens, toxins, or pathogens. For this reason, it is mandatory to develop highly sensitive, reliable, rapid, and cost-effective sensing systems/devices such as electrochemical sensors/biosensors. Generally, conventional techniques are limited by long time of analyses, expensive and complex procedures, and they require skilled personnel. Therefore, the development of performant electrochemical biosensors can significantly support the screening of food chain and products. Here, we report some of the recent developments in this area and analyze the contributions produced by electrochemical biosensors in the food screening and the challenges to address.

Whole cell biosensors could serve as eco-friendly and cost-effective alternatives for detecting potentially toxic bioavailable heavy metals in aquatic environments. However, they often fail to meet practical requirements due to an insufficient limit of detection (LOD) and high background noise. In this study, we designed a synthetic genetic circuit specifically tailored for detecting ionic mercury, which we applied to environmental samples collected from artisanal gold mining sites in Peru. We developed two distinct versions of the biosensor, each utilizing a different reporter protein: a fluorescent biosensor (Mer-RFP) and a colorimetric biosensor (Mer-Blue). Mer-RFP enabled real-time monitoring of the culture’s response to mercury samples using a plate reader, whereas Mer-Blue was analysed for colour accumulation at the endpoint using a specially designed, low-cost camera set up for harvested cell pellets. Both biosensors exhibited negligible baseline expression of their respective reporter proteins and responded specifically to HgBr2 in pure water. Mer-RFP demonstrated a linear detection range from 1 nM to 1 μM, whereas Mer-Blue showed a linear range from 2 nM to 125 nM. Our biosensors successfully detected a high concentration of ionic mercury in the reaction bucket where artisanal miners produce a mercury-gold amalgam. However, they did not detect ionic mercury in the water from active mining ponds, indicating a concentration lower than 3.2 nM Hg2+ -a result consistent with chemical analysis quantitation. Furthermore, we discuss the potential of Mer-Blue as a practical and affordable monitoring tool, highlighting its stability, reliance on simple visual colorimetry, and the possibility of sensitivity expansion to organic mercury.

The booming wearable market and recent advances in material science has led to the rapid development of the various wearable sensors, actuators, and devices that can be worn, embedded in fabric or accessories, or tattoos directly onto the skin. Wearable actuators, a subcategory of wearable technology, have attracted enormous interest from researchers in various disciplines and many wearable actuators and devices have been developed in the past few decades to assist and improve people's everyday lives. In this paper, we review the actuation mechanisms, structures, applications, and limitations of recently developed wearable actuators including pneumatic and hydraulic actuators, shape memory alloys and polymers, thermal and hygroscopic materials, dielectric elastomers, ionic and conducting polymers, piezoelectric actuators, electromagnetic actuators, liquid crystal elastomers, etc. Examples of the recent applications such as wearable soft robots, haptic devices, and personal thermal regulation textiles are highlighted. Finally, we point out the current bottleneck and suggest the prospective future research directions for wearable actuators.

Transdermal alcohol biosensors have the ability to detect the alcohol that emanates from the bloodstream and diffuses through the skin. However, they have suffered from long-term fouling of the sensor element and drift in the resulting sensor readings over time. Here, we report a disposable cartridge platform that solves the problem of sensor fouling, and an enzymatic detection pathway that minimizes baseline drift for sensitive detection. Laboratory characterization of the enzymatic alcohol sensor demonstrates a linear sensor range of between 0 and 50 mM. Further, we show continuous transdermal alcohol data recorded with a human subject over 48 hours.

Identifying punch types as part of punch kinematic analysis is a key component for both coaches and players, providing critical insights into the variety and effectiveness of punches, which are essential for refining overall performance. Existing research indicates that punch type classification typically relies on data from either wearables or video data. Both approaches have their merits and demerits but require a significant amount of labeled data for modeling the classification algorithm. In this work, we propose a novel approach that significantly reduces the labeling effort by one-sixth by leveraging video data to enhance the training information of sensor data. This enriched training phase enables more accurate punch classification with reduced labeled data. Subsequently, the real-time punch identification and classification use only the wearable data. On a real dataset, with 15% of the labeling efforts, our approach achieved an accuracy rate of 91.41% for rear hand punch recognition and 91.91% for lead hand punch recognition, along with 92.33% and 94.56% for punch classification, respectively. Through the development of our Smart Boxer system, we aim to revolutionize punch analytics in boxing, providing comprehensive insights while enhancing the overall training and competitive experience for athletes and enriching fan engagement with the sport.

The rapid and sensitive detection of pathogenic bacteria is becoming increasingly important for the timely prevention of contamination and treatment of infections. Biosensors based on nucleic acid aptamers integrat-ed with optical, electrochemical, and mass-sensitive analytical techniques have garnered intense interest for their versatility, cost-efficiency, and ability to exhibit high affinity and specificity in binding bacterial bi-omarkers, toxins, and whole cells. This review emphasizes the development of aptamers, their structural op-timization, and their use in bacterial sensing applications. Moreover, we explore barriers and develop per-spectives in applying aptamer-based bacterial detection.

Bioelectrocatalysis provides the intrinsic catalytic-functions of redox enzymes to non-specific electrode reactions and is the most important and basic concept for biosensors. This review starts by describing fundamental characteristics of bioelectrocatalytic reactions in mediated and direct electron transfer types from a theoretical viewpoint and summarizes amperometric biosensors based on multi-enzymatic cascades and for multi-analyte detection. The review also introduces prospective aspects of two new concepts of biosensors: mass-transfer-controlled (pseudo)steady-state amperometry at microelectrodes with enhanced enzymatic activity without calibration curves and potentiometric coulometry at enzyme/mediator-immobilized biosensors for absolute determination.

Objectives. To compare the audiological benefits of a non-implantable wearing option of a bone conduction (BC) implant mounted on an arch (SoundArc) to traditional BC hearing aids (HA) mounted on eyeglasses (BCHA) in patients with moderate to severe conductive or mixed hearing loss. Methods: A preliminary cross-sectional observational prospective cohort study was conducted at a Tertiary Audiological Department, University Hospital. Fourteen adults with conductive or mixed hearing loss (PTA at 0.5-1-2-4 KHz = 67 ± 15 dB HL) had been wearing conventional BCHA on eyeglasses for at least 3 years, and refused the surgical implantation of a BC hearing implant (BCHI). Unaided and aided pure-tone AC and BC thresholds and speech test in quiet and in noise were recorded at baseline and in two different settings: with a BCHI mounted on SoundArc®, and with their own BCHA with two couplers, mounted on eyeglasses. International Inventory for Hearing Aids [IOI – HA], the Hearing Handicap Inventory [HHIA/E], Speech, Spatial, Qualities of Hearing Scale [SSQ], a 10-scores visuo-analogic scale [VAS], and Fatigue impact scale [FIS] score were asked in both conditions. Results: a statistical significative functional gain was observed for both settings. (p=.0001) Better speech perception performances in quiet and in noise were observed with SoundArc compared to their conventional BCHA on eyeglasses (word repetition score in noise improvement of +19.3 at SNR +10 dB, p=.002; + 12.1 at SNR 0 dB, p=.006; and +11.4 at SNR -10 dB, p=.002) No significant differences were found in IOI–HA, FIS, and HHIA/E scores, while significative better SSQ scores in all domains were reported for SoundArc. (p=.0038) Conclusions: Although patients were accustomed to their BCHA on eyeglasses, the non-implanted wearable option of the BCHI proved to be a viable alternative in adult patients with conductive or mixed hearing loss unable to receive or refusing BCHI surgery.

Several eye-tracking technologies have been developed in the last 20 years. This paper aims to present a new kind of eye tracker that can produce detailed information on both eye and head movements using an array of magnetoresistive detectors fixed on the patient’s head and a small magnet inserted in a contact lens, tailored to the subject’s cornea curvature. The software used for data analysis can combine or compare the eye and the head movements and can draw them up as 2D or 3D images. Preliminary data concern a starter patient that was asked to perform several tasks to establish the accuracy, reliability and tolerance of the magnetic eye tracker and the software. The tasks included the evaluation of saccadic eye movements and pursuit, “drawing” shapes or alphabetical letters and reading. Finally, a Head Impulse Test (HIT) was performed for VOR gain estimation, comparing the standard deviation established through vHIT to the one established through this magnetic eye tracker (mHIT). This prototypic device is low invasive, light, relatively low cost and tolerable, with a high grade of reliability and accuracy. All these features might lead to future use of the magnetic eye tracker in neurological and otoneurological fields.

Graphene quality for use in biosensors was assessed in fabricated chips by a set of methods that includes atomic force microscopy (AFM), Raman spectroscopy, and low-frequency noise. It is shown that local areas of residues on the graphene surface, which arisen due to the interaction of graphene with a photoresist at the initial stage of chips development, leads to a spread of chips resistance (R) within 1-10 kΩ and to an increase in the root mean square (RMS) roughness up to 10 times that can significantly impair reproducibility of graphene parameters in biosensors chips. It was observed that the control of the photoresist residues after photolithography (PLG) by AFM and subsequent additional cleaning allow reducing the spread of R values in chips to 1–1.6 kΩ and obtaining RMS roughness similar to the roughness in pristine graphene before PLG. Monitoring of the spectral density of low-frequency voltage fluctuation (SU), which provides integral information about the defect system and quality of the material, makes it possible to identify chips with low graphene quality and with inhomogeneously distributed compressive stresses areas by the type of frequency dependence SU (f).

Fluorescent hydrogels are promising candidate materials for portable biosensors to be used in point-of-care diagnosis because (1) they have a greater capacity for binding organic molecules than immunochromatographic test systems, determined by the immobilization of affinity labels within the three-dimensional hydrogel structure; (2) fluorescent detection is more sensitive than the colorimetric detection of gold nanoparticles or stained latex microparticles; (3) the properties of the gel matrix can be finely tuned for better compatibility and detection of different analytes; and (4) hydrogel biosensors can be made reusable and suitable for studying dynamic processes in real time. Water-soluble fluorescent nanocrystals are widely used for in vitro and in vivo biological imaging due to their unique optical properties, and hydrogels based on them allow preserving these properties in bulk composite macrostructures. Here we review the techniques for obtaining analyte-sensitive fluorescent hydrogels based on nanocrystals, the main methods used for detecting the fluorescent signal changes, and the approaches to the formation of inorganic fluorescent hydrogels via sol–gel phase transition using surface ligands of the nanocrystals.

The deadliest disease in the world, cancer, kills many people every year. The early detection is the only hope for the survival of malignant cancer patients. As a result, in the preliminary stages of , the diagnosis of cancer biomarkers at the cellular level is critical for improving cancer patient survival rates. For decades, scientists have focused their efforts on the invention of biosensors. Biosensors, in addition to being employed in other practical scenarios, can essentially function as cost effective and highly efficient devices for this purpose. Traditional cancer screening procedures are expensive, time-consuming, and inconvenient for repeat screenings. Biomarker-based cancer diagnosis, on the other hand, is rising as one of the most potential tools for early detection, disease progression monitoring, and eventual cancer treatment. As Biosensor is an analytical device, it allows the selected analyte to bind to the biomolecules being studied (– for example RNA, DNA, tissue, proteins, cells). They can be divided based on the kind of biorecognition or transducer elements on the sensor. Most biosensor analyses necessitate the analyte being labeled with a specific marker. In this review article, the application of distinct variants of biosensors against cancer has been described.

The development of biosensors with high sensitivity and low-detection limits provides a new direction for medical and personal care. Graphene and graphene derivatives have been used to prepare various types of biosensors due to their excellent sensing performance (e.g. high specific surface area, extraordinary electronic properties, electron transport capabilities and ultrahigh flexibility). This perspective review focuses on graphene-based biosensors for quantitative detection of cancer related biomarkers such as DNA, miRNA, small molecules and proteins by integrating with different signal outputting approaches including fluorescent, electrochemistry, surface plasmon resonance, surface enhanced Raman scattering etc. The article also discussed their challenges and potential solutions along with future prospects.

Thanks to advanced semiconductor microfabrication technology, chip-scale integration and miniaturization of lab-on-a-chip components, silicon-based optical biosensors have made significant progress for the purpose of point-of-care diagnosis. In this review, we provide an overview of the state-of-the-art in evanescent field biosensing technologies including interferometer, microcavity, photonic crystal, and Bragg grating waveguide-based sensors. Their sensing mechanisms and sensor performances, as well as real biomarkers for label-free detection, are exhibited and compared. We also review the development of chip-level integration for lab-on-a-chip photonic sensing platforms, which consist of the optical sensing device, flow delivery system, optical input and readout equipment. At last, some advanced system-level CMOS-chip packaging examples are presented, indicating the commercialization potential for the low cost, high yield, portable biosensing platform leveraging CMOS processes.

The utilization of inertial measurement units (IMUs) as wearable sensors is proliferating 1 across various domains such as healthcare, sports, and rehabilitation. This expansion is driven by 2 the continuous improvement of sensor technology to accommodate specific operational demands. 3 This paper introduces the development of a configurable wearable device capable of capturing both 4 high-frequency and low-frequency signals, tailored for a wide range of monitoring applications.Our 5 focus is on a comprehensive evaluation of the device in terms of deviation, noise levels, and precision. 6 We present initial findings that demonstrate the device’s robust performance, and put on the spotlight 7 the most critical optimizations needed to improve the device to reach the goal of a smart general-use 8 unit for human motion monitoring.

Biosensors are indispensable tools to follow plant’s immunity as its spatiotemporal dimension is key in withstanding the complex plant immune signaling. The diversity of genetically encoded biosensors in plants is expanding, covering new analytes with ever higher sensitivity and robustness, but their assortment is limited in some aspects, such as their use to follow biotic stress response, employing more than one biosensor in the same chassis and their implementation into crops. In this review, we focused on the available biosensors that encompass these aspects. We show that in vivo imaging of calcium and reactive oxygen species is satisfactorily covered with the available genetically encoded biosensors, while on the other hand they are still underrepresented when it comes to imaging of the main three hormonal players of the immune response, salicylic acid, ethylene and jasmonic acid. Following more than one analyte in the same chassis, upon one or more conditions has so far been possible by using the most advanced genetically encoded biosensors in plants which allow to monitor calcium and two main hormonal pathways involved in plant development, auxin and cytokinin. These kinds of biosensors are also the most evolved in crops. In the last section, we gathered the challenges in the use of the biosensors and showed some strategies to overcome them.

Antibiotic resistance and accordingly their pollution because of uncontrolled usage has emerged as a serious problem in recent years. Hence, there is an increased demand to develope robust, easy, and sensitive methods for rapid evaluation of antibiotic and their residues. Among different analytical methods, the aptamer-based biosensors (aptasensors) have attracted considerable attention because of good selectivity, specificity, and sensitivity. This review gives an overview about recent developed aptasensors for antibiotic detection. The use of various aptamer assays to determine different groups of antibiotics like β-lactams, Aminoglycosides, Anthracyclines, Chloramphenicol, (Fluoro)Quinolones, Lincosamide, Tetracyclines and Sulfonamides are presented in this paper.

The advancement of performance in the domain of flexible wearable strain sensors has become increasingly significant due to extensive research on laser-induced graphene (LIG). An innovative doping modification technique is required owing to the limited progress achieved by adjusting laser parameters to enhance LIG’s performance. By pre-treating with AgNO3, we successfully manufactured LIG with a uniform dispersion of silver nanoparticles across its surface. The experimental results for the flexible strain sensor exhibit exceptional characteristics, including low resistance (183.4 Ω), high sensitivity (426.8), a response time of approximately 150 ms, and a relaxation time of about 200 ms. Moreover, this sensor demonstrates excellent stability under various tensile strains and remarkable repeatability during cyclic tests lasting up to 8,000 s. Additionally, this technique yields favorable results in finger bending and hand back stretching experiments, holding significant reference value for preserving the inherent characteristics of LIG preparation in a single-step and in situ manner.

Global demand for sleep-tracking wearables, or consumer sleep technologies (CSTs), is steadily increasing. CST marketing campaigns often feature a scientific component, but the scientific relevancy and monetary value of CST features within the sleep research community remains unquantified. Sleep medicine experts were recruited through social media and nonprobability sampling techniques to complete a survey identifying sleep metrics and device features that are most desirable to the scientific community. A hypothetical purchase task (HPT) estimated economic valuation for devices with different features by price. Forty-six (N=46) respondents with an average of 10±6 years’ experience conducting research in real-world settings completed the online survey. Total sleep time was ranked as the most important measure of sleep followed by objective sleep quality while sleep architecture/depth and diagnostic information were ranked as least important. Experts preferred wrist-worn devices that could reliably determine sleep episodes as short as 20 minutes. Economic value was greater for hypothetical devices with longer battery life. These data set a precedent to determine how scientific relevance of a product impacts the potential market value of a CST device. This is the first known attempt to establish consensus opinion or economic valuation for scientifically-desirable CST features and metrics using expert elicitation.

Wearable thermoelectric generators (w-TEGs) convert thermal energy into electrical energy to realize self-powering of intelligent electronic devices, thus reducing the burden of battery replacement and charging, and improving the usage time and efficiency of electronic devices. Through finite element simulation, this study successfully designed high-performance thermoelectric generator and made it into wearable thermoelectric module by adopting “rigid device - flexible connection” method. It was found that higher convective heat transfer coefficient on cold-end leads to larger effective temperature difference and better power generation performance of device in typical wearable scenario. Meanwhile, at same convective heat transfer coefficient on the cold-end, longer TE leg length leads to larger temperature difference established at both ends of device, larger device output power and open-circuit voltage. However, when the convective heat transfer coefficient increases to a certain level, optimization effect of increasing TE leg length on device power generation performance will gradually diminish. For devices with fixed temperature difference between two ends, longer TE leg length leads to higher resistance of TEG, resulting in lower device output power but slight increase in open-circuit voltage. Finally, sixteen 16×4×2 mm2 TEGs (L=1.38 mm, W=0.6 mm) and two modules were fabricated and tested. At hot end temperature Th=33 ℃ and cold end temperature Tc=30 ℃, the actual maximum output power Pout of TEG is about 0.2 mW, and the actual maximum output power Pout of TEG module is about 1.602 mW, which is highly consistent with the simulated value. This work brings great convenience to research and development of wearable thermoelectric modules and provides new, environmentally friendly and efficient power solution for wearable devices.

Wearable computing is a fast evolving segment of computing that includes smart watches, head mounted wearables such as Magic Leap headsets, Microsoft Hololens, and VR goggles from various vendors. In this report, we present ideas for a smart wearable device that also doubles as a virus protection device. Instead of using the filtering approach that is predominantly used by virus protection equipments such as face masks, we propose to use a computational approach where the device maintains an awareness of the real-time virus spread and use that information to steer the wearer away from the virus. As the wearable has a head enclosing design, viral infection can only happen through the air that is inhaled by the wearer. The objective of the smart wearable is to maintain a repository for clean air and switch the operating modes between stored and fresh air modes depending on the environmental conditions. It can augment this basic operating procedure by recycling the exhaled air to maximize it operating capacity (i.e., time duration for which it could supply the wearer with safe air) and by cleaning the stored air using UVC to further reduces the chance of infection. To maintain an awareness of the virus spread in the environment, the smart wearable will rely on an edge computing framework that will be distributed to cover areas frequented by people. The smart wearable will have modular design so that it can be reconfigured to add or subtract functionality that the wearer wants for a particular situation so that the design remains relevant even after the virus threat recedes.

Tamm Plasmon Polariton (TPP) is a nanophotonic phenomenon that has attracted much attention due to its spatial strong field confinement, ease of mode excitation and polarization independence. TPP has applications in sensing, storage, lasing, perfect absorber, solar cell, nonlinear optics, and many others. In this work, we demonstrate a biosensing platform based on TPP resonant mode. Both theoretical analyses based on Transfer Matrix Method and experimental validation through nonspecific detection of liquids of different refractive indices and specific detection of SARS-CoV-2 nucleocapsid protein (N-protein) are presented. Results show that TPP biosensor has high sensitivity and good specificity. For N-protein detection, the sensitivity is 1.5 nm/(µg/ml) and the limit of detection can reach down to below 7 ng/ml with spectrometer of 0.01 nm resolution of wavelength shift. This work paves the way for design, optimization, fabrication, characterization, and performance analysis of TPP based biosensors

Electrochemical biosensors based on immobilized enzymes are among the most popular and commercially successful biosensors. Literature in this field suggests that modification of electrodes with nanomaterials is an excellent method for enzyme immobilization, which can greatly improve the stability and sensitivity of the sensor. However, the poor stability, weak reproducibility, and limited lifetime of the enzyme itself still limit the requirements for the development of enzyme electrochemical biosensors for food production process monitoring. Therefore, constructing sensing technologies based on enzyme electrochemical biosensors remains a great challenge. This article outlines the construction principles of four generations of enzyme electrochemical biosensors and discusses the applications of single-enzyme systems, multi-enzyme systems and nano-enzyme systems developed based on these principles. The article further describes methods to improve enzyme immobilisation by combining different types of nanomaterials such as metals and their oxides, graphene-related materials, metal-organic frameworks, carbon nanotubes and conducting polymers. In addition, the article highlights the challenges and future trends of enzyme electrochemical biosensors, providing theoretical support and future perspectives for further research and development of high-performance enzyme chemical biosensors.

Coagulant lectin from Moringa oleifera seeds (cMoL) was characterized by potentiometry and scanning electron microscopy (SEM) using MOF, Metal-Organic Frameworks of [Cu3(BTC)2.H2O)3]n, to immobilize cMoL and construct biosensors. Developed aluminum-air batteries degraded indigo carmine dye formulated similar to a textile effluent; biosensors investigated cMoL interaction with specific galactose and monitored residual dye. SEM revealed components of electrode assembly steps. Oxide reduction reactions of batteries generated Al(OH)3 promoting dye electrocoagulation. Cyclic voltammetry showed differentiated redox peaks related to dye residue quantification by cMoL. Electrochemical systems evaluated cMoL interaction with ligand and efficiently degraded dye; biosensors could be used for lectin characterization and monitoring dye residues in environmental textile effluents.

: Microbial fuel cell (MFCs) devices utilise the metabolic processes of bacteria to generate electricity from various organic substrates. Due to the enormous amount of energy that organic waste produces, scientists are highly interested in advanced MFCs. MFCs serve as a multidisciplinary research platform at the engineering and natural sciences intersection. MFCs have various uses besides being used as energy sources, such as sensing capabilities. Despite showing considerable promise, only a few marine sediment MFCs have been deployed in real-world applications to supply current for low-power devices. It is now required to maintain track of the work being done by research groups worldwide and regularly compile significant discoveries due to the rising quantity of research publications. Review papers are a traditional beginning point for a literature review for new scholars. This review is a fast reckoner that directs readers to pertinent reviews and research publications detailing significant advances in microbial fuel cell research during the previous two decades. An overview of key advances in MFC research over the past two decades is provided as a quick reference in this review article. In addition, the report identifies research gaps that, if filled, could bring this technology closer to real-world use.

Candida albicans is an opportunistic fungus that becomes pathogenic and problematic under certain biological conditions. C.albicans may cause painful and uncomfortable symptoms, as well as deaths in immunocompromised patients. Therefore, early detection of c.albicans is essential. However, conventional detection methods are costly, slow and inaccessible to women in remote or developing areas. To address these concerns, we have developed a wearable and discrete naked-eye detectable, colorimetric platform for c.albicans detection. With some modification, this platform is designed to be directly adhered to existing feminine hygiene pads. Our platform is rapid, inexpensive, user-friendly, disposable and only requires three steps: i) addition of vaginal fluid onto sample pads; ii) addition of gold nanoparticle gel and running buffer, and iii) naked eye detection. Our platform is underpinned by selective thiolated aptamer-based recognition of 1,3-β-D glucan molecules- a hallmark of c.albicans cell walls. In the absence of c.albicans, wearable sample pads turn bright pink. In the presence of c. albicans, the wearable pads turn dark blue due to significant nanoparticle target-induced aggregation. We demonstrate naked eye colorimetric detection of 4.4 ×106 c.albicans cells per ml. We believe that this proof-of-concept platform has the potential to have a significant impact on women’s health, globally.

Biosensors are measurement devices that can sense several biomolecules, and are widely used for the detection of relevant clinical pathogens such as bacteria and viruses, showing outstanding results. Because of the latent existing risk of facing another pandemic like the one we are living due to COVID-19, researchers are constantly looking forward to developing new technologies for diagnosis and treatment of infections caused by different bacteria and viruses. Regarding that, nanotechnology has improved biosensors design and performance through the development of materials and nanoparticles that enhance their affinity, selectivity, and efficacy in detecting these pathogens, such as employing nanoparticles, graphene quantum dots, and electrospun nanofibers. Therefore, this work aims to present a comprehensive review that exposes how biosensors work in terms of bacterial and viral detection, and the nanotechnological features that are contributing to achieving a faster yet still efficient COVID-19 diagnosis at the point-of-care.

Chitosan is a biopolymer derived from chitin. It is a non-toxic, biocompatible, bioactive, and biodegradable polymer. Due to its properties, chitosan has found applications in several and different fields such as agriculture, food industry, medicine, paper fabrication, textile industry, and water treatment. In addition to these properties, chitosan has a good film-forming ability which allows it to be widely used for the development of sensors and biosensors. This review is focused on the use of chitosan for the formulation of electrochemical chemosensors. It also aims to provide an overview of the advantages of using chitosan as an immobilization platform for biomolecules by highlighting its applications in electrochemical biosensors. Finally, applications of electro-chemical chitosan-based chemosensors and biosensors in food safety are illustrated

The development of sensitive and specific exosome detection devices is highly desirable because it is believed to deliver specific important information for cancer screening, diagnosis, and monitoring. Among many detection techniques, nanoplasmonic sensors are optical detection methods that offer advantages in sensitivity and detection speed and this makes the sample analysis process faster and more accurate. This paper summarizes the development of nanoplasmonic biosensors, especially Surface Plasmon Resonance (SPR) and Localized Surface Plasmon Resonance (LSPR)-based biosensors for exosome detection. There are three main parts in this paper. The first part will focus on introducing the theoretical background regarding the working principles of SPR and LSPR biosensors. After that, we will introduce several methods that can be used for exosome isolation. This article will close by explaining the engineering of nanoplasmonic sensors both in terms of transducer modification to amplify the signal and functionalization strategies to increase sensor specificity. In this section, the detection mechanism, the type of exosomes detected, and the resulting detection limits are some of the points highlighted in this paper.

Early diagnosis and treatment have always been highly desired in the fight against cancer, and detection of circulating tumor DNA (ctDNA) has recently been touted as highly promising for early cancer screening. Consequently, the detection of ctDNA in liquid biopsy gains much attention in the field of tumor diagnosis and treatment, which has also attracted research interest from the industry. However, traditional gene detection technology is difficult to achieve low cost, real-time and portable measurement of ctDNA. Electroanalytical biosensors have many unique advantages such as high sensitivity, high specificity, low cost and good portability. Therefore, this review aims to discuss the latest development of biosensors for minimal-invasive, rapid, and real-time ctDNA detection. Various ctDNA sensors are reviewed with respect to their choices of receptor probes, detection strategies and figures of merit. Aiming at the portable, real-time and non-destructive characteristics of biosensors, we analyze their development in the Internet of Things, point-of-care testing, big data and big health.

This paper provides an overview of flexible and wearable respiration sensors with emphasis on their significance in healthcare applications. The paper classifies these sensors based on their operating frequency distinguishing between high-frequency sensors which operate above 10 MHz and low-frequency sensors. The operating principles of breathing sensors as well as the materials and fabrication techniques employed in their design are addressed. The research highlights the need for robust and flexible materials to enable the development of reliable and comfortable sensors. Finally, the paper presents potential research directions and proposes research challenges in the field of flexible and wearable respiration sensors. Thus, identifying emerging trends and gaps in knowledge, it encourages further advancements and innovation in the rapidly evolving domain of flexible and wearable sensors.

Emotions are indicators of affective states and play a significant role in human daily life, behavior, and interactions. Giving emotional intelligence to the machines could, for instance, facilitate early detection and prediction of (mental) diseases and symptoms. Electroencephalography (EEG) -based emotion recognition is being widely applied because it measures electrical correlates directly from the brain rather than the indirect measurement of other physiological responses initiated by the brain. The recent development of non-invasive and portable EEG sensors makes it possible to use them in real-time applications. Therefore, this paper presents a real-time emotion classification pipeline, which trains different binary classifiers for the dimensions of Valence and Arousal from an incoming EEG data stream. After achieving a 23.9% (Arousal) and 25.8% (Valence) higher f1-score on the state-of-art AMIGOS dataset, this pipeline was applied to the dataset achieved by an emotion elicitation experimental framework developed within the scope of this thesis. Following two different protocols, 15 participants were recorded using two different consumer-grade EEG devices while watching 16 short emotional videos in a controlled environment. For an immediate label setting, the mean f1-score of 87% and 82% were achieved for Arousal and Valence, respectively. In a live scenario, while continuously being updated on the incoming data stream with delayed labels, the pipeline proved to be fast enough to achieve predictions in real time. However, the significant discrepancy from the readily available labels on the classification scores leads to future work to include more data with frequent delayed labels in the live settings.

This paper describes the successful application of the Transformer model used in the natural language processing and vision tasks as a means of processing the time series of signals from gyroscope and accelerometer sensors for the classification of human activities. The Transformer model is based on deep neural networks with many layers which can generalize well on signals. All measured signals come from a smartphone placed in a waist bag. Activity prediction is sequence-to-sequence, each time step of the signal is assigned a designation of the performed activity. Emphasis is placed on attention mechanisms, which express individual dependencies between signal values within a time series. In comparison with another recent result, the recognition precision was improved from 89.67 percent to 99.2 percent. The transformer model should in the future be included among the top options in machine learning methods for human activity recognition.

In this research, we developed a wearable temperature-sensing element by dip dyeing threads in poly (3, 4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) (p-type conducting polymer) solution. The PEDOT:PSS was used to dye the textile and it exhibited negative temperature coefficient characteristics in which the resistance decreases as the temperature increases. The fabricated temperature-detection thread achieved a sensitivity of 167.1 W/°C with 99.8% linearity in the temperature range of -50 to 80 °C. We anticipate that temperature sensors that apply our technology will be made as stitch- or textile-type for wearable devices, and they will be widely adopted for different applications such as in fitness, leisure, healthcare, medical treatment, infotainment, industry, and military applications, among others.

The recent growth of wearable medical device technology in the form of fitness trackers, smart watches, smartphone apps, and patient monitoring systems has created people-generated health data (PGHD) that may benefit medical science with large amounts of continuous real-world data. The prevalence of these devices speaks to their broad popularity and user-friendliness and may lead us one day to a more fully “connected healthcare system.” Meanwhile, data security, confidentiality, and privacy issues have emerged in these hackable systems. Despite the promise of anonymized data, data can sometimes be re-identified, but even without that step, data breaches may reveal information (name, address, date of birth, social security number, and so on) sufficient for identity theft. Clinicians are often asked about the utility and value of wearable devices or monitors, but most are unaware that data from these systems may be transmitted, stored, and even sold without the user’s specific knowledge. Despite the confidentiality of medical information, cybersecurity surrounding wearables and monitors remains relatively lax, making them comparatively easy targets for cyber-villains. It is also important that efforts be taken to make PGHD more secure, since medical data may have great value to telehealth applications and AI-physician assistants. Clinicians should take an active role in informing patients about both risks and benefits of wearables and similar devices.

Osteoarthritis (OA) poses a growing challenge for the aging population, especially in hip and knee joints, contributing significantly to disability and societal costs. Conventional clinical evaluations, typically performed in controlled settings, face difficulties in capturing the dynamic characteristics of rehabilitation and functioning in real-life scenarios. The study assessed the use of automated unsupervised evaluations in the rehabilitation process for patients after hip and knee replacements. We analyzed data from 1144 individuals who used a digital application after surgery, activity data were collected using the Garmin Vivofit 4. Several parameters such as the Peak 6-Minute Consecutive Cadence (P6MC) and Peak 1-Minute Cadence (P1M) were computed and analyzed on a daily basis. The results indicated that cadence measurements, specifically, can effectively differ among individuals with hip and knee conditions as well as the recovery process. Moreover, these metrics demonstrated a high level of responsiveness in monitoring the progress of recovery, surpassing the effectiveness of simply measuring the total number of steps taken. The study highlighted the capacity of technology-driven, patient-centered rehabilitation to improve accuracy, tailor therapy to individual patients, and increased patient involvement. This research also highlighted the significance of automated, unsupervised assessments in enhancing rehabilitation results for OA patients following hip and knee replacement, as tele-rehabilitation becomes increasingly important.

Field-effect transistor (FET)-based biosensors stand as powerful analytical tools for detecting trace-specific biomolecules in diverse sample matrices, especially in the realms of pandemics and infectious diseases. The primary concern in applying these biosensors is their stability, a factor directly impacting the accuracy and reliability of sensing over extended durations. The risk of biosensor degradation is substantial, potentially jeopardizing sensitivity and selectivity and leading to inaccurate readings, including the possibility of false positives or negatives. This paper delves into the documented degradation of silicon nanobelt FET (NBFET) biosensors induced by buffer solutions. The results highlight a positive correlation between immersion time and the threshold voltage of NBFET devices. Secondary ion mass spectrometry analysis demonstrates a gradual increase in sodium and potassium ion concentrations within the silicon as immersion days progress. This outcome is ascribed to the nanobelt's exposure to the buffer solution during the biosensing period, enabling ion penetration from the buffer into the silicon. The study emphasizes the critical need to address buffer solution-induced degradation to ensure the long-term stability and performance of FET-based biosensors in practical applications.

We describe a machine learning (ML) approach to process the signals collected from Covid-19 optical-based detector. Multilayer Perceptron (MLP) and Support Vector Machine (SVM) were used to process both raw data and feature engineering data, and high performances for qualitative detection of the SARS-CoV-2 virus with concentration down to 1 TCID50/ml has been achieved. Valid detection experiments contain 486 negative and 108 positive samples; and control experiments, in which biosensors without antibody functionalization were used to detect SARS-CoV-2, contains 36 negative samples and 732 positive samples. Data distribution patterns of the valid and control detection dataset, based on T-distributed Stochastic Neighbor Embedding (t-SNE), was used to study the distinguishability between positive and negative samples, and explain the ML prediction performances. This work demonstrates that ML can be a generalized effective approach to process signals and dataset of biosensors dependent on resonant modes as biosensing mechanism.

Recent developments in optical biosensors based on integrated photonic devices are reviewed with a special emphasis on silicon-on-insulator ring resonator. The review is mainly devoted to the following aspects: (1) Principles of sensing mechanism, (2) sensor design, (3) biofunctionalization procedures for specific molecule detection and (4) measurement set-ups and advances in chip-integration. The inherent challenges of implementing photonics-based biosensors to meet specific requirements of applications in medicine, food analysis, and environmental monitoring are discussed.

Almost 50 papers on surface plasmon resonance biosensors, published between 2016 and mid-2018, are reviewed. Papers concerning the determination of large particles such as vesicles, exosomes, cancer cells, living cells, stem cells and microRNA are excluded, as these are covered by a very recent review. The reviewed papers are categorized into five groups, depending on the degree of maturity of the reported solution: ranging from simple marker detection to clinical application of a previously developed biosensor. Instrumental solutions and details of biosensor construction are analyzed, including the chips, receptors and linkers used, as well as calibration strategies. Biosensors with a sandwich structure containing different nanoparticles are considered separately, as are SPR applications for investigating the interactions of biomolecules. An analysis is also made of the markers determined using the biosensors. Concluding, there is shown to be a growing number of SPR applications in the solution of real clinical problems.

The article reviews the current status of label-free integrated optical sensors focusing on the evolution over the years of their analytical performance. At first a short introduction to the evanescent wave optics is provided followed by detailed description of the main categories of label-free optical sensors including sensors based on surface plasmon resonance (SPR), grating coupler, photonic crystals, ring resonators and interferometric transducers. After a short reference to SPR techniques, including localized SPR, integrated optical sensors i.e., grating-couplers, interferometers, photonic crystals, microring resonators are reviewed. For each type of sensor, the detection principle is first provided followed by description of the different transducer configurations so far developed and their performance as biosensors. Finally, a short discussion about the current limitations and future perspectives of integrated label-free biosensors optical sensors is provided

Rehabilitation from musculoskeletal injuries focuses on reestablishing and monitoring muscle activation patterns to accurately produce force. The aim of this study is to explore the use of a novel low-powered wearable distributed Simultaneous Musculoskeletal Assessment with Real-Time Ultrasound (SMART-US) device to predict force during an isometric squat task. Participants (N=5) performed maximum isometric squats under two medical imaging techniques; clinical musculoskeletal motion mode (m-mode) ultrasound on the dominant vastus lateralis and SMART-US sensors placed on the rectus femoris, vastus lateralis, medial hamstring, and vastus medialis. Ultrasound features were extracted, and a linear ridge regression model was used to predict ground reaction force. The performance of ultrasound features to predict measured force was tested using either the Clinical M-mode, SMART-US sensors on the vastus lateralis (SMART-US: VL), rectus femoris (SMART-US: RF), medial hamstring (SMART-US: MH), and vastus medialis (SMART-US: VMO) or utilized all four SMART-US sensors (Distributed SMART-US). Model training showed that the Clinical M-mode and the Distributed SMART-US model were both significantly different from the SMART-US: VL, SMART-US: MH, SMART-US:RF, and SMART-US:VMO models (p<0.05). Model validation showed that the Distributed SMART-US model had an R² of 0.80 ±0.04 and was significantly different from SMART-US: VL but not from the Clinical M-mode model. In conclusion, a novel wearable distributed SMART-US system can predict ground reaction force using machine learning, demonstrating the feasibility of wearable ultrasound imaging for ground reaction force estimation.

Focal vibration therapy can provide neurophysiological benefits. Unfortunately, standardized protocols are non-existent. Previous research presents a wide range of protocols with a wide range of effectiveness. This paper is part of a broader effort to identify effective, standardized protocols for focal vibration therapy. The vibration characteristics of four commercially available focal vibration devices that have been used for research and clinically were measured. An accelerometer was used for the measurements. Frequency and peak-to-peak amplitude were measured. Measurements were made when the devices were free and then again when they were strapped to the human body. Vibration frequency ranged from 120 to 225 Hz. Free vibration amplitude ranged from 2.0 to 7.9 g’s (peak-to-peak). When the devices were strapped to the body (constrained), vibration amplitude decreased by up to 65.7%. These results identify effective ranges of focal vibration frequency and amplitude. They illustrate the importance of identifying vibration environment, free or constrained, when quoting vibration characteristics. Finally, the inconsistency of multi-actuator devices is discussed. These results will guide protocol development for focal vibration and potentially better focal vibration devices.

A sensitive and robust electrochemical cholinesterase-based sensor was developed to detect the quaternary ammonium (QAs) biocides most frequently found in agri-food industry wash waters: benzalkonium chloride (BAC) and didecyldimethylamonium chloride (DDAC). To reach the maximum residue limit of 2.8 10-8 M imposed by the European Union (EU), two types of cholinesterases were tested, acetylcholinesterase (AChE, from Drosophila melanogaster) and butyrylcholinesterase (BChE, from horse serum). The sensors were designed by entrapping AChE or BChE on cobalt-phthalocyanine-modified screen-printed carbon electrodes. The limits of detection (LOD) of the resulting biosensors were 3.8 10-8 M for DDAC and 3.2 10-7 M for BAC, using respectively AChE and BChE. A simple solid phase extraction step was used to concentrate the samples before biosensor analysis, allowing the accurate determination of DDAC and BAC in tap water with limits of quantification (LOQ) as low as 1.3 10-9 M and 5.3 10-9 M, respectively. The biosensor was shown to be stable during 3-months storage at 4°C.

Exoskeletons designed to assist patients with activities of daily living are becoming increasingly popular, but still are subject to research. In order to gather requirements for the design of such systems, long-term gait observation of the patients over the course of multiple days in an environment of daily living are required. In this paper a wearable all-in-one data acquisition system for collecting and storing biomechanical data in everyday life is proposed. The system is designed to be cost efficient and easy to use, using off-the-shelf components and a cloud server system for centralized data storage. The measurement accuracy of the system was verified, by measuring the angle of the human knee joint at walking speeds between 3 and 12 km/h in reference to an optical motion analysis system. The acquired data was uploaded to a cloud database via a smartphone application. Verification results showed that the proposed toolchain works as desired. The system reached an RMSE from 2.9° to 8°, which is below most of the comparable system. The system provides a powerful, scalable platform for collecting and processing biomechanical data, which can help to automize the generation of an extensive database for human kinematics.

Demand for green technologies and energy is in continuous growth in the last years. Compact efficient antennas are crucial for energy harvesting portable systems. Small antennas have low efficiency. The efficiency of communication and energy harvesting systems may be improved by using efficient passive and active antennas. The system dynamic range may be improved by connecting amplifiers to the printed antenna feed line. Design, design considerations, computed and measured results of wearable meta-materials antennas with high efficiency for energy harvesting applications are presented in this paper. All antennas were analyzed by using 3D full-wave software. The antennas electrical parameters on human body are presented. The directivity and gain of the antennas with Split-ring resonators, SRR, is higher by 2.5dB than the antennas without SRR. The resonant frequency of the antennas with SRR is lower by 5% to 10% than the antennas without SRR.

Biosensors based on an oxygen electrode, a mediator electrode and a mediator microbial biofuel cell (MFC) using on the bacteria Gluconobacter oxydans B-1280 have been formed and tested to determine the integral toxicity. G. oxydans bacteria exhibit high sensitivity to the toxic effects of phenol, 2,4-dinitrophenol, salicylic and trichloroacetic acid and a number of heavy metal ions. The system “G. oxydans bacteria – ferrocene – graphite-paste electrode” is superior in sensitivity to biosensors formed using an oxygen electrode and MFC, in particular regarding heavy metal ions (EC50 of chromium (II), manganese (II) and cadmium (II) was 0.8 mg/dm3, 0.3 mg/dm3 and 1.6 mg/dm3, respectively). It has been determined that the period of stable functioning of electrochemical systems during measurements is reduced by half due (from 30 to 15 days) due to changes in the enzyme system of microbial cells when exposed to toxicants. Samples of products made from polymeric materials were analyzed using developed biosensor systems and standard biotesting methods based on inhibiting the growth of duckweed L. minor, reducing the motility of bull sperm and quenching the luminescence of the commercial test system "Ecolum". The developed bioelectrocatalytic systems are comparable in sensitivity to commercial biosensors, which made it possible to correlate the results and identify by all methods a highly toxic sample containing diphenylmethane-4,4'-diisocyanate according to GC-MS data.

Analytical chemistry applied to medical and diagnostic analysis, recently focused on the development of cost-effective biosensors able to monitor the health status or to assess the level of specific biomarkers that can be indicative for several diseases. The improvement of technologies relating to the possibility of non-invasive sampling of biological fluids, as well as sensors for the detection of analytical signals and the computational capabilities of the systems routinely employed in everyday life (e.g. smartphones, computers, etc.), make the complete integration of self-standing analytical devices more accessible. This review aims to discuss the biosensors that have been proposed in the last 5 years focusing on two principal detecting approaches, optical and electrochemical, which have been employed for quantifying different kind of target analytes reaching detection limits below the clinical sample levels required. These detection principles applied to Point-of-care (POC) devices, have been extensively reported in literature and even the limited examples found on the market are based on these strategies. This work will show the latest innovations considering the integration of optical and electrochemical detection with the most commonly reported analytical platforms for POC applications such as paper-based or wearable and implantable devices.

Cancer has been considered one of the most serious diseases in recent decades. Early diagnosis of cancer is a crucial step for an expedited treatment. Ideally, detection of cancer biomarkers, which are usually elevated because of cancer, is the most straightforward approach to detect cancer. Consequently, the accurate, effective, and prompt detection of these compounds is an insistent need for the medical diagnosis of the disease in order to start an early therapy plan for the patients. Among these biomarkers, Carcinoembryonic antigen (CEA) is considered one of the most important tumor markers for colorectal cancer, but it has been also used as a biomarker for other types of cancers, including breast, gastric, ovarian, pancreatic, and lung cancers. Typically, conventional CEA testing depends on immunoassay approaches, which are known to be complex, highly expensive, and time-consuming. In this context, many biosensors were designed for the aim of detecting cancer biomarkers. The main prerequisites of these biosensors are high sensitivity, fast response, and low cost. Many nanostructures have been involved in the design of biosensors. Metal organic frameworks (MOFs) were found to be one of the most potential and promising materials for biosensing. MOFs are highly porous and crystalline materials that consist of metal clusters surrounded by organic linkers, where the assembly of these components gives rise to the exquisite geometric 3D structures of MOFs. Moreover, the unique structure and geometry of MOFs allow for a better tailoring of their design to provide properties that are needed by different categories of biosensors. In the past few years, researchers have extensively considered MOFs for their fabrication of biosensors that can be used for the early detection of cancer biomarkers. In this regard, MOFs were used solely or were further decorated with other nanostructures to introduce more accurate signals and lower limits of detection. This review briefly classifies and describes MOFs-based biosensors trials that have been published recently for the aim of detecting CEA.

The aim of this study is to develop a software solution for real-time recognition of sign language words using two arms. This will enable communication between hearing-impaired individuals and those who can hear. Several sign language recognition systems have been developed using different technologies, including cameras, armbands, and gloves. The system developed in this study utilizes surface electromyography (muscle activity) and inertial measurement unit (motion dynamics) data from both arms. Other methods often have drawbacks, such as high costs, low accuracy due to ambient light and obstacles, and complex hardware requirements, which have prevented their practical application. A software has been developed that can run on different operating systems using digital signal processing and machine learning methods specific to the study. For the test, we created a dataset of 80 words based on their frequency of use in daily life and performed a thorough feature extraction process. We tested the recognition performance using various classifiers and parameters and compared the results. The Random Forest algorithm was found to have the highest success rate with 99.875% accuracy, while the Naive Bayes algorithm had the lowest success rate with 87.625% accuracy. Feedback from a test group of 10 people indicated that the system is user-friendly, aesthetically appealing, and practically useful. The new system enables smoother communication for people with hearing disabilities and promises seamless integration into daily life without compromising user comfort or lifestyle quality.

Research into improving the functionality and capacities of wearable technology has surged as a result of its fast progress. The functioning of wearable technology is largely dependent on antenna design, which provides wireless connectivity and communication. This study of the literature searches many online sources for pertinent research on the various antenna designs for wearable technology. The findings verified that the materials vary based on the design being employed. An analysis was conducted on the gain, frequency, materials, and sizes. The review excels at synthesizing research and literature despite its restricted resources.

The need for miniaturized shear force sensors is expanding, particularly for biomedical applications. Examples include measuring interfacial shear stresses between a human and an external device (e.g., footwear or a prosthesis). However, there are considerable challenges in designing a shear sensor for these applications due to the need for a small package, low power requirements, and resistance to interference from motion artifact and electromagnetic fields. This paper presents the design, fabrication, and characterization sensor that measures two-axis shear force by detecting displacement between a color panel and a red, green, and blue light-sensing photodiode. The sensor response to applied displacements and forces was characterized under benchtop testing conditions. We also present the design of a prototype wireless version of the sensor for integration into footwear. The sensor exhibited strong agreement with gold standard measurements for two axis shear displacements (R2>0.99, RMSE≤5.0 µm) and forces (R2>0.99, RMSE≤0.94 N). This performance, along with the sensor’s scalability, miniaturized form, and low power requirements make it well-suited a variety of biomedical applications.

Background: Natural environment inhalation topography provides useful information for toxicant exposure, risk assessment and cardiopulmonary performance. Commercially available Wearable Respiratory Monitors (WRM), which are currently used to measure a variety of physiological parameters such as heart rate and breathing frequency, can be leveraged to obtain inhalation topography, yet little work has been done. This paper assesses the feasibility of adapting these WRMs for measuring inhalation topography. Methods: Commercially available WRMs were compiled and assessed for the ability to report chest motion, data analysis software features, ambulatory observation capabilities, participant acceptability, purchasing constraints and affordability. Results: The following WRMs were found: LifeShirt, Equivital EQ02 LifeMonitor, Smartex WWS, Hexoskin Smart Garment, Zephyr BioHarness, Nox T3&amp;A1, BioRadio, SleepSense Inductance Band, and ezRIP &amp; zRIP Durabelt. None of the WRMs satisfied all six assessment criteria in a manner enabling them to be used for inhalation topography without modification and development. Conclusion: The results indicate that there are WRMs with core technologies and characteristics that can be built upon for ambulatory inhalation topography measurement in the NE.

As the rapid development of communication industry, especially the 5G communication and IOT, there will be plenty of electromagnetic (EM) waves in the free space, which carry lots of energies. However, some of these energies are wasted in free space. To reuse these wasted energies, significantly growing interests are towards to the energy harvesting antennas. This paper is aimed to investigate a wearable antenna which can transfer RF energy from ambient sources to direct current by a soft and portable textile antenna. Among the numerous signals in mobile network, the GSM 1800, 3G, WiFi and 4G/5G will be chosen due to the city signal intensity distribution. Hence, a corresponding triple bands antenna has been designed to cover those frequency bands. The CST STUDIO SUITE is used in whole process of antenna design and simulation. The proposed antenna is a hooked dipole antenna with tuning bar (HATB) whose ends have small folded part in each side for better bandwidth performance. The presented antenna provides a wide operating band from 1.8 GHz to 2.5 GHz below -10 dB in return loss. And methods to overcome interference between antennas is found. Furthermore, using the corresponding rectifier to achieve the RF/DC conversion. The overall efficiency of whole rectifier is about 56.8 %, and output power level of the antenna system is 45.92nW. The experimental results could indicate that my textile hooked antenna harvester is a good choice for the charging system of personal wearable attachment, which could achieve low power absorbing for long distance anytime and anywhere.

Recent developments in flexible printed heaters (FPH) for wearable thermal applications, driven by the advancement of printed electronics, show great promise in revolutionizing patient care through the development of wearable flexible heaters for medical applications. Wearable heaters with high thermal stability, heat uniformity, safety, flexibility, comfort, biocompatibility, and power efficiency are desirable for stand-alone medical thermotherapy applications. This paper reviews recent advancements in the design of FPHs for wearable thermal applications. Materials for FPHs, fabrication methods, designs, temperature control mechanisms, medical applications, and performance analysis of specific FPHs are all thoroughly discussed. Materials for FPHs, such as conductive and substrate materials, have received special attention, along with the heater design parameters. Finally, the challenges and future directions for developing FPHs for wearable medical applications are addressed.

Nanotechnology and polymer engineering are steering towards a new development to invade deleterious mysteries. In the last few decades, polymer engineering has grabbed researchers’ attention and similarly, polymeric network-engineered structures have been vastly studied. Prior to therapeutic application, early and quick detection analyses are critical. Therefore, developing nanoscale sensors to manage the acute expression of feeble states and malignancies to draw therapeutic lines demands advanced nanoengineering. In spite of nano-therapeutics have emerged as an alternative approach to tackling strenuous diseases. Also, studies have shown highly biocompatible biomedical engineering has emerged with sheer progression. Moreover, hydrogels have emerged as a three-dimensional (3D) polymeric network that consists of hydrophilic natural or synthetic polymers. The resemblance of hydrogels with tissue structure makes it more unique to study inquisitively. Preceding studies have shown a vast spectrum of synthetic and natural polymer deployment in the field of biotechnology and molecular diagnostics. This review explores recent studies on synthetic and natural polymer engineered hydrogel-based biosensors and their applications in multipurpose diagnostics and therapeutics. We reviewed the latest studies on hydrogel-engineered biosensors, exclusively DNA-based and DNA hydrogels fabricated biosensors.

Analyzing horse behavior is crucial for assessing training quality, particularly in accurately identifying actions like standing, walking, and running. Research has been conducted both domestically and internationally in this regard; however, challenges persist, including reliance on single sensors, poor real-time performance, and low identification accuracy. In view of these challenges, this study investigated real-time identification of horse behavior based on wearable devices. The system, centered around a microcontroller and utilizing a 4G network as a carrier, employs multi-axis IMU sensors as input sources to perceive horse posture. The study proposed a behavior classification method based on analysis of acceleration thresholds. The method consists primarily of two sequential stages: first, the resultant acceleration interval counting method, which employs a nonlinear segmentation approach for initial behavior classification; and second, the statistical analysis of variance parameters between segments, which when coupled with multi-level threshold processing, achieves a refined classification. Experimental results indicated that the interval counting method alone achieved an accuracy of 87.55%, while for the variance analysis method alone the accuracy was 90.87%. The proposed method, comprising the two stages, reached a classification accuracy of 91.57%, underpinning its usefulness in supporting future equine research.

In this article we present the design and development of T-Shirt prototypes that embed novel textile sensors for the capture of cardio and respiratory signals. The sensors are connected through textile interconnects to either an embedded custom designed data acquisition and transmission unit or to snap fastener terminals for connection to external monitoring devices. Prototypes with diverse approaches of integration are presented. The performance of the wearable systems is addressed in terms of signal-to-noise ratio amplitude and signal interference caused by baseline wander and motion artifacts, through laboratorial tests with subjects in standing and walking conditions. Performance tests were also conducted in Hospital environment using a T-Shirt prototype connected to a commercial 3-channel Holter monitoring device. The textile sensors and interconnects were realized with the assistance of an industrial 6-needle digital embroidery tool and their resistance to wear addressed with normalized tests of laundering and abrasion. The main aspects of the system´s design leading to major improvements and failure factors are discussed. Pathways and methods for the overall system´s optimization are highlighted.

In the era of nanotechnology, researchers are implementing point to care service to cancer patients to detect malignancy beforehand and to reduce the mortality rate of cancers. Cancer is known to be the most fatal disease among all other diseases and the survivability from cancer is quite impossible if the stage of the cancer is an advanced level. Though the early detection of cancer can increase the chances of survival with a double fold. Biosensor is a part of nanotechnology which is capable to provide point to care service in the field of medicine. With the rising number of cancer occurrences being identified around the world and the increasing number of deaths because of the identification of advanced cancer, biosensors can play a significant part in the early detection of cancer. New molecular methods, including as genomic and proteomic approaches, are increasingly being used to study patient molecular profiles. When such diagnosis method is paired with bioinformatics tools, they generate new data that can be used to discover new disease biomarkers. Finding precise and sensitive indicators that are corelated to a specific disease, as with many other diseases, can be challenging. Furthermore, the concentration of biomarkers in biological fluids varies according to illness states and phases. Peptides, proteins, up or down regulated expression of gene markers, and gene alternation are all examples of molecular markers that are commonly used to diagnose cancer. In this article, we have highlighted six different deadliest cancers such as Ovarian, Breast, Prostrate, Lung, Colorectal and Liver cancer. The article contains distinct types of biomarkers which are normally found in these kinds of cancer and generally used as a potential diagnostic target in the medicine field. The article mainly summarized the application of different types of biosensors devices in the detection of the mostly found biomarkers in the above cancer types.

Prevalence estimates of Parkinson’s disease (PD) – the fastest growing neurodegenerative disease – are underestimated due to issues surrounding diagnostic accuracy, symptomatic undiagnosed cases, suboptimal prodromal monitoring, and limited screening access. Remotely monitored wearable devices and sensors provide precise, objective, and frequent measures of motor and non-motor symptoms. Here, we used consumer-grade wearable device and sensor data from the WATCH-PD study to develop a PD screening tool aimed at eliminating gaps between patient symptoms and diagnosis. Early-stage PD (n=82) and age-matched comparison (n=50) participants completed a multidomain assessment battery during a one-year longitudinal multicenter study. Using disease- and behavior-relevant feature engineering and multivariate machine learning modeling of early-stage PD status, we developed a highly accurate (92.3%), sensitive (90.0%), and specific (100%) Random Forest classification model (AUC=0.92) that performed well across environmental contexts. These findings provide robust support for further exploring consumer-grade wearable devices and sensors for global population-wide PD screening and surveillance.

Stress has been identified as one of the major causes of automobile crashes which then lead to high rates of fatalities and injuries each year. Stress can be measured via physiological measurements and in this study the focus will be based on the features that can be extracted by common wearable devices. Hence the study will be mainly focusing on the heart rate variability (HRV). This study is aimed to develop a good predictive model that can accurately classify stress levels from ECG-derived HRV features, obtained from automobile drivers, testing different machine learning methodologies such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Random Forest (RF) and Gradient Boosting (GB). Moreover, the models obtained with highest predictive power will be used as reference for the development of a machine learning model that would be used to classify stress from HRV features derived from HRV measurements obtained from wearable devices. We demonstrate that MLP was the ideal stress classifier by achieving a Recall of 80%. The proposed method can be also used on all applications in which is important to monitor the stress level e. g. in physical rehabilitation, anxiety relief or mental wellbeing.

Wearable exoskeleton solutions for upper limb rehabilitation or assistance, particularly for the hand area, have become increasingly attractive to researchers, proving to be effective over time in treating hand movement impairments following various neurological diseases. In the present work, the development of a wearable exoskeleton-type device for active hand rehabilitation has been addressed, based on fluid actuators made of elastomeric material and on myoelectric control by capturing myoelectric signals from the forearm area. The flexion movement of the fingers (phalanges) is actively assisted by pneumatic actuation of the built-in actuators and the extension movement is assisted by a depressurization control of the actuator. The device has been designed to be as easy to use as possible, light in weight, and to provide a high degree of comfort when used by the patient for rehabilitation training or daily life activities (ADL), which can be performed in the comfort of one's own home.

Background: medical devices are designed, tested and placed on the market in a highly regulated environment. Wearable sensors are crucial components of various medical devices: design and validation of wearable sensors, if managed according to international standards, can foster innovation while respecting regulatory requirements. Material and methods: the purpose of this paper is to take into consideration the upcoming EU Medical Device Regulation 2017/245 and the current and future IEC and ISO standards that set methods for design and validation of medical devices, with a focus on wearable sensors. Risk classification according to the regulation is described. The international standards IEC 62304, IEC 60601, ISO 14971 and ISO 13485 are reviewed to define regulatory restrictions during design, pre-clinical validation and clinical validation of devices that include wearable sensors as crucial components. Results: current and future regulatory restrictions are described, and an integrated method for design planning, validation and clinical testing is described Discussion: application of this method to design wearable sensors should be evaluated in the future in order to assess its potentially positive impact to fostering innovation and to the time-to-market of the device.

Animal viruses are a significant threat to animal health and are easily spread across the globe with the rise of globalization. The limitations in diagnosing and treating animal virus infections have made the transmission of diseases and animal deaths unpredictable. Therefore, early diagnosis of animal virus infections is crucial to prevent the spread of diseases and reduce economic losses. To address the need for rapid diagnosis, electrochemical sensors have emerged as a promising tool. Electrochemical methods present numerous benefits, including heightened sensitivity and selectivity, affordability, ease of use, portability, and rapid analysis, making them suitable for real-time virus detection. This paper focuses on the construction of electrochemical biosensor, as well as promising biosensor models, and expounds its advantages in virus detection, which is a promising research direction.

Pesticides are chemical products are used to protect crops to prevent or eliminate undesirable organisms. Pesticides can contaminate continental waters, air, soil and food products. The presence of pesticide residues must therefore be monitored in water and food using sensitive, reliable and broad-spectrum analytical methods, as there is a huge variety of pesticides. Biosensors are one of the screening methods with great potential for detecting residues below the maximum residue limits. The different sensing principles of enzymatic biosensors for pesticide analysis will be described. This review highlights the great potential of colorimetric, enzymatic biosensors for detecting pesticide residues in water and food. Moreover the development of dual colorimetric and fluorimetric sensors during the past years is presented. These methods are most often based on the enzyme acetylcholinesterase, whose activity is inhibited by pesticides. The different approaches used in the development of these biosensors will be detailed, based on different chemical substrates, fluorophores and nanomaterials. The advantages and disadvantages of each approach will be discussed. Finally, the development prospects for improving these biosensors will be presented.

Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease. Technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which include RT-PCR, antigen-antibody and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include Deep Learning and Transfer learning approach. The review also provide comparison between these 2 emerging technologies and open research issues for the development of smart-IoMT-enable platform for the detection of COVID-19.

Wearable antennas are increasingly vital in modern healthcare, facilitating continuous monitoring and communication for medical purposes. These antennas are integrated into wearable devices, allowing for the transmission and reception of vital health data, such as heart rate, body temperature, and blood pressure, to medical professionals or monitoring systems in real time. This instantaneous data transmission enables timely interventions, thus improving the quality of healthcare services. This review aims to comprehensively synthesize recent research from reputable databases, focusing on the functionality and effectiveness of wearable antennas in medical applications. The primary objectives of each study were categorized, including antenna design and integration, the performance of medical wearable antenna technology, safety considerations in wearable antenna biomedicine, and the use of antennas in clinical applications and their impact. This synthesis provides valuable insights for researchers and practitioners aiming to develop and deploy wearable antennas in medical settings, ultimately enhancing healthcare delivery and patient outcomes.

Wearable touch sensors, which can convert force or pressure signals into quantitative electronic signals, have emerged as an essential smart sensing devices and played an important role in various cutting-edge fields, including wearable health monitoring, soft robots, electronic skin, artificial prosthetics, AR/VR, and the Internet of Things. Flexible touch sensors have made significant advancements, while construction of novel touch sensors via mimicking the unique properties of biological materials and biogenetic structures always remains a hot research topic and significant technological pathway. This review provides a comprehensive summary of the research status of wearable touch sensors constructed by imitating the material and structural characteristics in nature, and also summarizes the scientific challenges and development tendency of this aspect. Firstly, the research status for constructing flexible touch sensors based on biomimetic materials is summarized, including hydrogel materials, self-healing materials, and other bioinspired or biomimetic materials with extraordinary properties. Then, design and fabrication of flexible touch sensors based on bionic structures for performance enhancement are fully discussed. These bionic structures include special structures in plants, special structures insects/animal, and special structures in human body. Moreover, a summary of the current issues and future prospects for developing wearable sensors based on bioinspired materials and structures is discussed.

Interviews from strength and conditioning coaches across all levels of athletic competition identified their two biggest concerns with the current state of wearable technology: (a) the lack of solutions that accurately capture data "from the ground up" and (b) the lack of trust due to inconsistent measurements. The purpose of this research is to investigate the use of liquid metal sensors, specifically Liquid Wire sensors, as a potential solution for accurately capturing ankle complex movements such as plantar flexion, dorsiflexion, inversion, and eversion. Sensor stretch linearity was validated using a Micro-Ohm Meter and a Wheatstone bridge circuit. Sensors made from different substrates were also tested and discovered to be linear at multiple temperatures. An ankle complex model and computing unit for measuring resistance values were developed to determine sensor output based on simulated plantar flexion movement. The sensors were found to have a significant relationship between the positional change and the resistance values for plantar flexion movement. The results of the study ultimately confirm the researchers' hypothesis that liquid metal sensors, and Liquid Wire sensors specifically, can serve as a mitigating substitute for inertial measurement unit (IMU) based solutions that attempt to capture specific joint angles and movements.

Cervical cancer is the most common female reproductive tract tumor and is currently the only cancer that can be prevented by vaccination. Early detection and treatment of cervical cancer can be achieved through regular screening for human papillomavirus (HPV). Therefore, in this paper, an electrochemical sensor was designed to detect HPV 16 using a dual signal amplification technique. APTES modified glassy carbon electrode to improve stability. Combining gold nanoparticles and chain amplification reactions for signal amplification purposes. The limit of detection (LOD) was 1.731×10-16 mol/L, and the detection range of this electrochemical sensor was increased in comparison to previous research, with a linear response range from 1.0×10-13 mol/L to 1.0×10-5 mol/L (R2 = 0.99232) for the target detector.In addition, the sensor showed equally good recovery in sophisticated human serum samples. It shows that it has good interference resistance and the designed biosensor can be a powerful and reliable tool for clinical disease diagnosis.

Stress has various impacts on the health of human beings. Recent success in wearable sensor development, combined with advancements in deep learning to automatically detect features from raw data, opens several interesting applications related to detecting emotional states. Being able to accurately detect stress-related emotional arousal in an acute setting can positively impact the imminent health status of humans, i.e., through avoiding dangerous locations in an urban traffic setting. This work proposes an explainable deep learning methodology for the automatic detection of stress in physiological sensor data, recorded through a non-invasive wearable sensor device, the Empatica E4 wristband. We propose an Long-Short Term-Memory (LSTM) network, extended through a Deep Generative Ensemble of conditional GANs (LSTM DGE) , to deal with the low data regime of sparsely labeled sensor measurements. As explainability is often a main concern of deep learning models, we leverage Integrated Gradients (IG) to highlight the most essential features used by the model for prediction and to compare the results to state-of-the-art expert-based stress detection methodologies in terms of precision, recall and interpretability. Results show that our LSTM DGE outperforms the state-of-the-art algorithm by 3 percentage points in terms of recall, and 7.18 percentage points in terms of precision. More importantly, through the use of Integrated Gradients as a layer of explainability, we show that there is a strong overlap between model-derived stress features for electrodermal activity, and existing literature, which current state-of-the-art stress detection systems in medical research and psychology are based on.

Ischemia, the inadequate blood flow, can arise from pre-existing or traumatic events, remain an undetectable issue, and impact the body's ability to provide oxygen to necessary limbs, organs, muscles, or tissue. New technology involving wearable ultrasonic devices allows medical professionals to gain real-time insight into the cardiovascular system of their patients. A non-invasive, postage-stamp-sized ultrasonic sensor can be attached to a patient, allowing for continuous monitoring of the patient’s blood pressure outside of the hospital. After reviewing 16 papers on ultrasonic sensors, this paper intends to review the current use of wearable ultrasonic sensors and the advantages and limitations of the technology used to obtain accurate blood pressure readings. This review will bring attention to the evolving and expanding world of wearable ultrasonic sensors for medical applications.

In recent years, several Ultrawideband (UWB) localization systems have already been proposed and evaluated for accurate position estimation of pedestrians. However, most of them are evaluated for a particular wearable sensor position; hence the accuracy obtained is subject to a given wearable sensor position. In this paper, we study the effects of body wearable sensor positions i.e., chest, arm, ankle, wrist, thigh, fore-head, hand, on the localization accuracy. The conclusion drawn is that the fore-head is the best, and the chest is the worst body sensor location for tracking a pedestrian. While the fore-head position is able to set an error lower than 0.35 m (90th percentile), the chest is able to set 4 m. The reason for such a contrast in the performance lies in the fact that in NLOS situations, the chest as an obstacle is larger in size and thickness than any other part of the human body, which the UWB signal needs to overcome to reach the target wearable sensor. And so, the large errors arise due to the signal arriving at the target wearable sensor from reflections of a nearby object or a wall in the environment.

Fiber-based flexible sensors have promising applications potential in human motion and healthcare monitoring, owing to their merits of lightweight, flexibility, and easy processing. Now, high-performance elastic fiber-based strain sensors with high sensitivity, large working range and excellent durability are in great demand. Herein, we have easily and quickly prepared a highly-sensitive, durable fiber-based strain sensor by dip-coating of highly-stretchable polyurethane (PU) elastic fiber in MXene/ waterborne polyurethane (WPU) dispersion solution. Benefiting from the electrostatic repulsion force between the negatively charged WPU and MXene sheets in the mixed solution, very homogeneous and stable MXene/WPU dispersion was successfully obtained and the interconnected conducting networks were correspondingly formed in coated MXene/WPU shell layer, which makes as-prepared strain sensor exhibit a gauge factor of over 960, a large sensing range of over 90% and detection limit as low as 0.5% strain. As elastic fiber and mixed solution have the same polymer constitute, tight bonding of MXene/WPU conductive composite on PU fibers was achieved, enabling the as-prepared strain sensor endure over 2500 stretching-releasing cycles and thus show good durability. Full-scale human motion detection was also performed by the strain sensor, and a body posture monitoring, analysis, and correction prototype system was developed via embedding the fiber-based strain sensors into sweater, strongly indicating the great application prospect in exercise, sports and healthcare.

Motor functions are deteriorated by aging. Some conditions may magnify this deterioration. To examine whether hemodialysis (HD) process would negatively impact gait and balance beyond diabetes condition among mid-age adults (48-64 years) and older adults (65+ years). One hundred and ninety-six subjects (age=66.2±9.1 years, body-mass-index=30.1±6.4 kg/m², female=56%) in 5 groups were recruited: mid-age adults with diabetes undergoing HD (Mid-age HD+, n=38) and without HD (Mid-age HD-, n=40); older adults with diabetes undergoing HD (Older HD+, n=36) and without HD (Older HD-, n=37); and non-diabetic older adults (Older DM-, n=45). Gait parameters (stride velocity, stride length, gait cycle time, and double support) and balance parameters (ankle, hip, and center of mass sways) were quantified using validated wearable platforms. Groups with diabetes had overall poorer gait and balance compared to the non-diabetic group (p<0.050). Among people with diabetes, the HD+ had significantly worsened gait and balance when comparing to the HD- (Cohen’s effect size d=0.63-2.32, p<0.050). Between-group difference was more pronounced among older adults with the largest effect size observed for stride length (d=2.32, p<0.001). Results suggested that deterioration in gait speed among the HD+ was correlated with age (r=-0.440, p<0.001), while this correlation was diminished among the HD-. Interestingly, results also suggested that poor gait in the Older HD- related to poor balance, while no correlation was observed between poor balance and poor gait among the Older HD+. Using objective assessments, results confirmed that the presence of diabetes can deteriorate gait and balance, and this deterioration can be magnified by HD process. Among non-HD people with diabetes, poor static balance described poor gait. However, among people with diabetes undergoing HD, age was a dominate factor describing poor gait irrespective of static balance. Results also suggested feasibility of using wearable platforms to quantify motor performance during routine dialysis clinic visits. These objective assessments may assist in identifying early deterioration in motor function, which in turn may promote timely intervention.

This paper describes preliminary results of measuring the impact of human body movements on plants. In particular, we analyze the influence of eurythmic gestures of human actors on lettuce and beans. In an eight week experiment, we exposed rows of lettuce and beans to weekly eurythmic movements (similar to Qi Gong) of a eurythmist, while at the same time measuring changes in voltage between the roots and leaves of lettuce and beans using the plant spikerbox (https://backyardbrains.com/products/plantspikerbox). We compared this experimental group of vegetables with a control group of vegetables whose voltage differential was also measured while never being exposed to eurythmy. Using t-tests, we found a clear difference between the experimental and the control group which was also verified with a machine learning model. In other words, the vegetables showed a noticeably different pattern in electric potentials in response to eurythmic gestures.

In recent years, the application of Indoor Positioning Systems (IPS) has experienced a significant increase in demand, with the aging of the world’s population and their changing lifestyles. While outdoor positioning systems, such as the Global Positioning System (GPS), have significantly advanced over the years, indoor positioning has been restrained by the limitations of the employed technologies. This paper presents a hybrid wireless positioning system able to locate wearables indoor accurately. It is based on the Wi-Fi and Bluetooth technologies, by using trilateration to determine the position of Bluetooth low energy (BLE) wearable devices with an accuracy of up to 1.8 meters. A graphical user interface (GUI) was used to illustrate the performance of the proposed systems, by allowing users to visualize the captured data in two dimensions and three- dimensions in real-time.

The capability of measuring specific neurophysiological and autonomic parameters plays a crucial role in the objective evaluation of the human’s mental and emotional states. These human aspects are commonly known in scientific literature to be involved in a wide range of processes, such as the stress and arousal. These aspects represent a relevant factor especially in real and operational environment. Neurophysiological autonomic parameters, such as the Electrodermal Activity (EDA) and Photoplethysmographic data (PPG), have been usually investigated through research-graded devices, therefore resulting in a high degree of invasiveness, which could negatively interfere with the monitored user’s activity. For such a reason, in the last decade the recent consumer-grade wearable devices, usually designed for fitness tracking purposes, are receiving an increasing attention from the scientific community, being characterized by higher comfort, easiness of use and, therefore, by higher compatibility with dailylife environments. The present preliminary study aimed at assessing the reliability of a consumer wearable device, i.e., the Fitbit Sense, with respect to a research-graded wearable, i.e., the Empatica E4 wristband, and a laboratory device, i.e., the Shimmer GSR3+. The EDA and PPG were collected among 12 participants while performing multiple resting conditions. The results demonstrated that the EDA and PPG-derived features computed through the wearable and research devices were positively and significantly correlated, while the reliability of the consumer device resulted to be significantly lower.

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter $\mathsf{\gamma }$—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

With the availability of wearable health monitoring sensor modules like 3-Lead Electrocardiogram (ECG), Pulse Oximeter (SpO2), Galvanic Skin Response (GSR), Hall effect sensor (for measuring Respiratory Rate), Blood Pressure and Temperature measuring and sensing elements, it has now become possible to device a composite health status monitoring kit that can measure vital signs and other physiological parameters pertaining to human health in real time. Traditionally, the physiological parameters along with vital signs related examination was possible only in a hospitalized or ambulatory environment, however due to advances in sensing and embedded system technology and miniaturization of data acquisition and processing elements health monitoring has become possible even when individuals remain engaged in their day to day activities at the convenience of space and location. The patients or individuals subject to monitoring may suffer from a traumatic experience due to their medical condition and may need emergent incidence response and the critical care team may have to prepare for the treatment only after the patient arrives, which often is too late, as in case of cardiac arrests or severe injuries. The research focused on real-time health status monitoring and trauma scoring using standard physiological parameters along with standard telemetry protocols to make the critical care team aware of an emergent situation and prepare for a medical emergency. Vital signs and physiological parameters (heart rate, temperature, respiratory rate, and blood pressure, SpO2) were measured in real time from human subjects non-invasively. In order to enable monitoring of the patients engaged in day to day activities, errors due to the motion were removed using stationary wavelet transform correction (correlation coefficient of 0.9 after correction) and signals from various sensors were denoised, filtered and were encoded in a format suitable for further data analysis. A composite sensor kit capable of monitoring vital signs and physiological parameters can be very useful in incident response when an individual undergoes a traumatic experience related to stroke, cardiac arrest, fits or even injury, as along with monitoring information the kit can calculate scores related to trauma like the Injury Severity Score (ISS), National Early Warning Signs (NEWS), Revised Trauma Score (RTS). Trauma Injury Severity Score (TRISS), Probability of Survival (Ps) score. An open access database of vital signs and physiological parameters from Physionet, MIMIC 2 Numerics (mimicdb/numerics) database was used to calculate NEWS and RTS and to generate correlation and regression models using the vital signs/physiological parameters for a clinical class of patients with respiratory failure and admitted to Intensive Care Unit (ICU). NEWS and RTS scores showed no significant correlation (r = 0.25, p<0.001) amongst themselves, however together NEWS and RTS showed significant correlation with Ps (blunt) (r = 0.70, p<0.001). RTS and Ps (blunt) scores showed some correlation (r = 0.63, p<0.001) and NEWS score showed significant correlation (r = 0.79, p<0.001) with Ps (blunt) scores. Furthermore, since individuals have to be monitored regardless of location, these kits have to have a built-in capability to locate the individual so that the incident response team can locate the individual based on Global Positioning System coordinates (GPS). A Quantum GIS (Geographical Information System) application using real-time GPS coordinates (OpenStreetMap coordinates) was used to calculate the shortest path using QGIS Network Analysis tool to demonstrate the calculation of shortest path and direction to locate the nearest service provider in shortest time. Along with locating the nearest healthcare service provider, it would help if the critical care team could be made aware of the physiological parameters and trauma scores using standard protocols accepted across the globe. The physiological parameters from the sensors along with the calculated trauma scores were encoded according to a standard Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) coding system and International Code of Diseases (ICD) codes and the trauma information was logged to Electronic Health Records (EHR) using Fast Health Interoperability Resources (FHIR) servers. FHIR servers provided interoperable web services to log the event information in real time. It could be concluded that analytical models trained on existing datasets can help in analyzing a traumatic experience or an injury and the information can be logged using a standard telemetry protocol as a telemedicine initiative. These scores enable the healthcare service providers to estimate the extent of trauma and prepare for medical emergency procedures and find applications in general and military healthcare.

Flexible electronics have revolutionized the field by overcoming the rigid limitations of traditional devices, offering superior flexibility and adaptability. Conductive ink performance is crucial, directly impacting the stability of flexible electronics. While metal filler-based inks exhibit excellent conductivity, they often lack mechanical stability. To address this challenge, we present a novel conductive ink utilizing a ternary composite filler system: liquid metal and two micron-sized silver morphologies (particles and flakes). We systematically investigated the influence of filler type, mass ratio, and sintering process parameters on the composite ink's conductivity and mechanical stability. Our results demonstrate that flexible wires fabricated with the liquid metal/micron silver particle/micron silver flake composite filler exhibit remarkable conductivity and exceptional bending stability. Interestingly, increasing the liquid metal content results in a trade-off, compromising conductivity while enhancing mechanical performance. After enduring 5000 bending cycles, the resistance change in wires formulated with a 4:1 mass ratio of micron silver particles to flakes is only half that of wires with a 1:1 ratio. This study further investigates the mechanism governing resistance variation during flexible wire bending. Additionally, we observed a positive correlation between sintering temperature and pressure with the conductivity of flexible wires. The significance of sintering parameters on conductivity follows a descending order: sintering temperature, sintering pressure, and sintering time. Finally, we demonstrate the practical application of this technology by integrating the composite ink-based flexible wires with conductive polymer-based strain sensors. This combination successfully achieved the detection of human movements, including finger and wrist bending.

Atrial Fibrillation (AF) is known as one of the dominant cardiac disease that is associated with severe consequences such as risk of strokes. This cardiac arrhythmia can be efficiently diagnosed using EEG signals. However, AF episodes in EEG records are not always permanent for all subjects. In addition, lengthy subject preparation and ECG recording sessions makes this practice time consuming and not attractive for efficient diagnosis. One attractive solution for this problem will be the use of wearable Internet of Medical Things IoMT devices for EEG recording and AF detection. From another side the use machine learning for efficient diagnosis could provide high accurate diagnosis. However, the deployment of these wireless wearable devices faces the problems of limited processing capabilities and of energy scarcity. In this context, the design of energy efficient accurate on-sensor AF detection scheme using machine learning algorithms still requires a focused research effort. This paper proposes a wearable solution for ECG-based processing scheme for embedded AF detection. The proposed method achieved 100% sensitivity and 94.5% of specificity and accuracy of around 97.4 % for wide range of subjects while showing low energy-consumption. The obtained results show the efficiency of using Logistic Regression algorithm for accurate AF detection and attest about practical feasibility of the proposed approach in wearable IoMT devices.

For technical or medical applications, the knowledge of the exact kinematics of the human hand is key to utilizing its capability to handle and manipulate objects and to communicate with other humans or machines. The optimal relationship between the number of measurement parameters, measurement accuracy as well as complexity, usability and cost of the measuring systems is hard to find. Biomechanic assumptions, the concepts of a biomechatronic system and the mechatronic design process as well as commercially available components are used to develop a sensorized glove. The proposed wearable can measure 14 of 15 angular values of a simplified hand model introduced in this paper. Additionally, five contact pressure values at the fingertips and inertial data of the whole hand with a degree of freedom of six are gathered. Due to the modular design and a hand size examination based on anthropometric parameters, the concept of the wearable is applicable for a large variety of hand sizes and adaptable to different use cases. Validations show a combined root-mean-square error of 0.99° to 2.38° for the measurement of all joint angles at one finger, surpassing the human perception threshold and the current state of the art in science and technology for comparable systems.

In the last decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of Brain Computer Interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on Motor Imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims at systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.