research-article

Learning from less for better: semi-supervised activity recognition via shared structure discovery

Authors:

Sen WangAuthors Info & Claims

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Pages 13 - 24

https://doi.org/10.1145/2971648.2971701

Published: 12 September 2016 Publication History

Abstract

Despite the active research into, and the development of, human activity recognition over the decades, existing techniques still have several limitations, in particular, poor performance due to insufficient ground-truth data and little support of intra-class variability of activities (i.e., the same activity may be performed in different ways by different individuals, or even by the same individuals with different time frames). Aiming to tackle these two issues, in this paper, we present a robust activity recognition approach by extracting the intrinsic shared structures from activities to handle intra-class variability, and the approach is embedded into a semi-supervised learning framework by utilizing the learned correlations from both labeled and easily-obtained unlabeled data simultaneously. We use l_2,1 minimization on both loss function and regularizations to effectively resist outliers in noisy sensor data and improve recognition accuracy by discerning underlying commonalities from activities. Extensive experimental evaluations on four community-contributed public datasets indicate that with little training samples, our proposed approach outperforms a set of classical supervised learning methods as well as those recently proposed semi-supervised approaches.

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Cited By

Xiao ZTong HQu RXing HLuo SZhu ZSong FFeng L(2025)CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity RecognitionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.334429436:2(2690-2704)Online publication date: Feb-2025
https://doi.org/10.1109/TNNLS.2023.3344294
Zhang XCui SZhu TChen LZhou FNing H(2024)CASL: Capturing Activity Semantics Through Location Information for Enhanced Activity RecognitionIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2023.323806421:4(1051-1059)Online publication date: Jul-2024
https://doi.org/10.1109/TCBB.2023.3238064
Yin YXie LJiang ZXiao FCao JLu S(2024)A Systematic Review of Human Activity Recognition Based on Mobile Devices: Overview, Progress and TrendsIEEE Communications Surveys & Tutorials10.1109/COMST.2024.335759126:2(890-929)Online publication date: Oct-2025
https://doi.org/10.1109/COMST.2024.3357591
Show More Cited By

Index Terms

Learning from less for better: semi-supervised activity recognition via shared structure discovery
1. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Conferences

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 2016

1288 pages

ISBN:9781450344616

DOI:10.1145/2971648

General Chairs:
Paul Lukowicz
DFKI and University of Kaiserslautern
,
Antonio Krüger
DFKI and Saarland University
,
Program Chairs:
Andreas Bulling
Max Planck Institute for Informatics
,
Youn-Kyung Lim
KAIST
,
Shwetak N. Patel
University of Washington

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 September 2016

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Australian Research Council

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UbiComp '16

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UbiComp '16: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 12 - 16, 2016

Germany, Heidelberg

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UbiComp '16 Paper Acceptance Rate 101 of 389 submissions, 26%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Xiao ZTong HQu RXing HLuo SZhu ZSong FFeng L(2025)CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity RecognitionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.334429436:2(2690-2704)Online publication date: Feb-2025
https://doi.org/10.1109/TNNLS.2023.3344294
Zhang XCui SZhu TChen LZhou FNing H(2024)CASL: Capturing Activity Semantics Through Location Information for Enhanced Activity RecognitionIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2023.323806421:4(1051-1059)Online publication date: Jul-2024
https://doi.org/10.1109/TCBB.2023.3238064
Yin YXie LJiang ZXiao FCao JLu S(2024)A Systematic Review of Human Activity Recognition Based on Mobile Devices: Overview, Progress and TrendsIEEE Communications Surveys & Tutorials10.1109/COMST.2024.335759126:2(890-929)Online publication date: Oct-2025
https://doi.org/10.1109/COMST.2024.3357591
Dridi JAmayri MBouguila N(2024)Multi-Source Domain Adaptation Using Ambient Sensor DataApplied Artificial Intelligence10.1080/08839514.2024.242932138:1Online publication date: 19-Nov-2024
https://doi.org/10.1080/08839514.2024.2429321
Duan HWang SOjha VWang SHuang YLong YRanjan RZheng Y(2024)Wearable-based Behaviour Interpolation for Semi-supervised Human Activity RecognitionInformation Sciences10.1016/j.ins.2024.120393(120393)Online publication date: Mar-2024
https://doi.org/10.1016/j.ins.2024.120393
Ray AKolekar M(2024)Transfer learning and its extensive appositeness in human activity recognition: A surveyExpert Systems with Applications10.1016/j.eswa.2023.122538240(122538)Online publication date: Apr-2024
https://doi.org/10.1016/j.eswa.2023.122538
Imanzadeh STanha JJalili M(2024)Ensemble of deep learning techniques to human activity recognition using smart phone signalsMultimedia Tools and Applications10.1007/s11042-024-18935-0Online publication date: 1-Apr-2024
https://doi.org/10.1007/s11042-024-18935-0
Thakur DFortino G(2024)A Comprehensive Review of Deep Learning for Activity RecognitionActivity Recognition and Prediction for Smart IoT Environments10.1007/978-3-031-60027-2_4(67-95)Online publication date: 27-May-2024
https://doi.org/10.1007/978-3-031-60027-2_4
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://doi.org/10.1109/TMC.2021.3136853
Fallahzadeh RAshari ZAlinia PGhasemzadeh H(2023)Personalized Activity Recognition Using Partially Available Target DataIEEE Transactions on Mobile Computing10.1109/TMC.2021.307143422:1(374-388)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TMC.2021.3071434
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