Article
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DLCAS: A Deep Learning-Based CPR Action Standardization Method
Version 1
: Received: 30 May 2024 / Approved: 31 May 2024 / Online: 31 May 2024 (10:22:00 CEST)
How to cite: Li, Y.; Yin, M.; Wu, W.; Lu, J.; Liu, S.; Ji, Y. DLCAS: A Deep Learning-Based CPR Action Standardization Method. Preprints 2024, 2024052102. https://doi.org/10.20944/preprints202405.2102.v1 Li, Y.; Yin, M.; Wu, W.; Lu, J.; Liu, S.; Ji, Y. DLCAS: A Deep Learning-Based CPR Action Standardization Method. Preprints 2024, 2024052102. https://doi.org/10.20944/preprints202405.2102.v1
Abstract
In emergency situations, ensuring standardized Cardiopulmonary Resuscitation (CPR) actions is crucial. However, current Automated External Defibrillators (AEDs) lack methods to determine whether CPR actions are performed correctly, leading to inconsistent CPR quality. To address this issue, we introduce a novel method called Deep Learning-based CPR Action Standardization (DLCAS). This method involves three parts. First, it detects correct posture using OpenPose to recognize skeletal points. Second, it identifies a marker wristband with our CPR-Detection algorithm and measures compression depth, count and frequency using a depth algorithm. Finally, we optimize the algorithm for edge devices to enhance real-time processing speed. Extensive experiments on our custom dataset have shown that the CPR-Detection algorithm achieves a mAP0.5 of 97.04%, while reducing parameters to 0.20M and FLOPs to 132.15K. In a complete CPR operation procedure, the depth measurement solution achieves an accuracy of 90% with a margin of error less than 1 cm, while the count and frequency measurements achieve 98% accuracy with a margin of error less than 2 counts. Our method meets the real-time requirements in medical scenarios, and the processing speed on edge devices have increased from 8fps to 25fps.
Keywords
CPR; AED; depth measurement; object detection; OpenPose; edge computing
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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