research-article

Large-Scale Personalized Human Activity Recognition Using Online Multitask Learning

Authors:

Xu Sun,

Hisashi Kashima,

Naonori UedaAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 25, Issue 11

Pages 2551 - 2563

https://doi.org/10.1109/TKDE.2012.246

Published: 01 November 2013 Publication History

Abstract

Personalized activity recognition usually has the problem of highly biased activity patterns among different tasks/persons. Traditional methods face problems on dealing with those conflicted activity patterns. We try to effectively model the activity patterns among different persons via casting this personalized activity recognition problem as a multitask learning issue. We propose a novel online multitask learning method for large-scale personalized activity recognition. In contrast with existing work of multitask learning that assumes fixed task relationships, our method can automatically discover task relationships from real-world data. Convergence analysis shows reasonable convergence properties of the proposed method. Experiments on two different activity data sets demonstrate that the proposed method significantly outperforms existing methods in activity recognition.

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Yu HChen ZZhang XChen XZhuang FXiong HCheng X(2023)FedHAR: Semi-Supervised Online Learning for Personalized Federated Human Activity RecognitionIEEE Transactions on Mobile Computing10.1109/TMC.2021.313685322:6(3318-3332)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TMC.2021.3136853
Hussein AHajj H(2022)Domain Adaptation with Representation Learning and Nonlinear Relation for Time SeriesACM Transactions on Internet of Things10.1145/35029053:2(1-26)Online publication date: 15-Feb-2022
https://dl.acm.org/doi/10.1145/3502905
Xu XChikersal PDutcher JSefidgar YSeo WTumminia MVillalba DCohen SCreswell KCreswell JDoryab ANurius PRiskin EDey AMankoff J(2021)Leveraging Collaborative-Filtering for Personalized Behavior ModelingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34481075:1(1-27)Online publication date: 30-Mar-2021
https://dl.acm.org/doi/10.1145/3448107
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Large-Scale Personalized Human Activity Recognition Using Online Multitask Learning
1. Computing methodologies
  1. Machine learning

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Abstract
Human Activity Recognition (HAR) is an essential task in various applications such as pervasive healthcare, smart environment, and security and surveillance. The need to develop accurate HAR systems has motivated researchers to propose various ...
Highlights
- Adoption of unsupervised learning in wearable sensor-datasets.
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- Existing data ...

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Published In

IEEE Transactions on Knowledge and Data Engineering Volume 25, Issue 11

November 2013

243 pages

ISSN:1041-4347

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Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 November 2013

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Yu HChen ZZhang XChen XZhuang FXiong HCheng X(2023)FedHAR: Semi-Supervised Online Learning for Personalized Federated Human Activity RecognitionIEEE Transactions on Mobile Computing10.1109/TMC.2021.313685322:6(3318-3332)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TMC.2021.3136853
Hussein AHajj H(2022)Domain Adaptation with Representation Learning and Nonlinear Relation for Time SeriesACM Transactions on Internet of Things10.1145/35029053:2(1-26)Online publication date: 15-Feb-2022
https://dl.acm.org/doi/10.1145/3502905
Xu XChikersal PDutcher JSefidgar YSeo WTumminia MVillalba DCohen SCreswell KCreswell JDoryab ANurius PRiskin EDey AMankoff J(2021)Leveraging Collaborative-Filtering for Personalized Behavior ModelingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34481075:1(1-27)Online publication date: 30-Mar-2021
https://dl.acm.org/doi/10.1145/3448107
Ma HZhang ZLi WLu S(2021)Unsupervised Human Activity Representation Learning with Multi-task Deep ClusteringProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34480745:1(1-25)Online publication date: 30-Mar-2021
https://dl.acm.org/doi/10.1145/3448074
Chauhan JKwon YHui PMascolo C(2020)ContAuthProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34322034:4(1-23)Online publication date: 18-Dec-2020
https://dl.acm.org/doi/10.1145/3432203
Chen LZhang YPeng L(2020)METIERProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33810124:1(1-18)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.1145/3381012
Wijekoon AWiratunga N(2020)Personalised Meta-Learning for Human Activity Recognition with Few-DataArtificial Intelligence XXXVII10.1007/978-3-030-63799-6_6(79-93)Online publication date: 15-Dec-2020
https://dl.acm.org/doi/10.1007/978-3-030-63799-6_6
Peng LChen LYe ZZhang Y(2018)AROMAProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32142772:2(1-16)Online publication date: 5-Jul-2018
https://dl.acm.org/doi/10.1145/3214277
Hu CChen YHu LPeng X(2018)A novel random forests based class incremental learning method for activity recognitionPattern Recognition10.1016/j.patcog.2018.01.02578:C(277-290)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1016/j.patcog.2018.01.025
Liu XTan PLiu L(2018)STARS: Soft Multi-Task Learning for Activity Recognition from Multi-Modal Sensor DataAdvances in Knowledge Discovery and Data Mining10.1007/978-3-319-93037-4_45(571-583)Online publication date: 3-Jun-2018
https://dl.acm.org/doi/10.1007/978-3-319-93037-4_45
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A multitask learning model for online pattern recognition

Activity recognition using conditional random field

A survey on unsupervised learning for wearable sensor-based activity recognition

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