poster

OCEAN: a new opportunistic computing model for wearable activity recognition

Authors:

Yiqiang Chen,

Yang Gu,

Xinlong Jiang,

Jindong WangAuthors Info & Claims

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

Pages 33 - 36

https://doi.org/10.1145/2968219.2971453

Published: 12 September 2016 Publication History

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Abstract

Activities of Daily Living (ADL) recognition through wearable devices is an emerging research field. While, for many applications, recognition methods are faced with simultaneously dynamic changes in feature dimension, activity class and data distribution. Existing approaches mainly handle at most one of these three challenges, which significantly affects their performance. In this paper, we propose an Opportunistic Computing model for wEarable Activity recognitioN (OCEAN); by fusing random mapping, fuzzy clustering, and weight updating techniques, OCEAN can online adaptively adjust Single-hidden Layer Feedforward neural network's connection, structure and weight in a coherent manner. Experimental evaluations demonstrate that OCEAN improves the recognition accuracy by 5% to 15% compared to traditional approaches towards dynamic changes.

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Cited By

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Alam GMcChesney INicholl PRafferty J(2023)Open Datasets in Human Activity Recognition Research—Issues and Challenges: A ReviewIEEE Sensors Journal10.1109/JSEN.2023.331764523:22(26952-26980)Online publication date: 15-Nov-2023
https://doi.org/10.1109/JSEN.2023.3317645
Zhao JLi LDeng FHe HChen J(2022)Discriminant Geometrical and Statistical Alignment With Density Peaks for Domain AdaptationIEEE Transactions on Cybernetics10.1109/TCYB.2020.299487552:2(1193-1206)Online publication date: Feb-2022
https://doi.org/10.1109/TCYB.2020.2994875
Kwon HAbowd GPlötz T(2021)Complex Deep Neural Networks from Large Scale Virtual IMU Data for Effective Human Activity Recognition Using WearablesSensors10.3390/s2124833721:24(8337)Online publication date: 13-Dec-2021
https://doi.org/10.3390/s21248337
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Index Terms

OCEAN: a new opportunistic computing model for wearable activity recognition
1. Human-centered computing

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Information & Contributors

Information

Published In

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

September 2016

1807 pages

ISBN:9781450344623

DOI:10.1145/2968219

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

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 September 2016

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Funding Sources

National Natural Science Foundation of China
National Key Research and Development Plan

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UbiComp '16

Sponsor:

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

September 12 - 16, 2016

Germany, Heidelberg

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

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Cited By

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Alam GMcChesney INicholl PRafferty J(2023)Open Datasets in Human Activity Recognition Research—Issues and Challenges: A ReviewIEEE Sensors Journal10.1109/JSEN.2023.331764523:22(26952-26980)Online publication date: 15-Nov-2023
https://doi.org/10.1109/JSEN.2023.3317645
Zhao JLi LDeng FHe HChen J(2022)Discriminant Geometrical and Statistical Alignment With Density Peaks for Domain AdaptationIEEE Transactions on Cybernetics10.1109/TCYB.2020.299487552:2(1193-1206)Online publication date: Feb-2022
https://doi.org/10.1109/TCYB.2020.2994875
Kwon HAbowd GPlötz T(2021)Complex Deep Neural Networks from Large Scale Virtual IMU Data for Effective Human Activity Recognition Using WearablesSensors10.3390/s2124833721:24(8337)Online publication date: 13-Dec-2021
https://doi.org/10.3390/s21248337
Hoelzemann AVan Laerhoven KVan Laerhoven KTentori MWeibel NHealey JPloetz T(2020)Digging deeperProceedings of the 2020 ACM International Symposium on Wearable Computers10.1145/3410531.3414311(50-54)Online publication date: 4-Sep-2020
https://dl.acm.org/doi/10.1145/3410531.3414311
Wang JZheng VChen YHuang M(2018)Deep Transfer Learning for Cross-domain Activity RecognitionProceedings of the 3rd International Conference on Crowd Science and Engineering10.1145/3265689.3265705(1-8)Online publication date: 28-Jul-2018
https://dl.acm.org/doi/10.1145/3265689.3265705
Wang JChen YHu LPeng XYu P(2018)Stratified Transfer Learning for Cross-domain Activity Recognition2018 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PERCOM.2018.8444572(1-10)Online publication date: Mar-2018
https://doi.org/10.1109/PERCOM.2018.8444572
McMillan DBrown BLampinen AMcGregor MHoggan EPizza SMark GFussell SLampe Cschraefel mHourcade JAppert CWigdor D(2017)Situating WearablesProceedings of the 2017 CHI Conference on Human Factors in Computing Systems10.1145/3025453.3025993(3582-3594)Online publication date: 2-May-2017
https://dl.acm.org/doi/10.1145/3025453.3025993
Wang JChen YHao SFeng WShen Z(2017)Balanced Distribution Adaptation for Transfer Learning2017 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2017.150(1129-1134)Online publication date: Nov-2017
https://doi.org/10.1109/ICDM.2017.150

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