research-article

Group activity recognition using belief propagation for wearable devices

Authors:

Michael BeiglAuthors Info & Claims

ISWC '14: Proceedings of the 2014 ACM International Symposium on Wearable Computers

Pages 3 - 10

https://doi.org/10.1145/2634317.2634329

Published: 13 September 2014 Publication History

Abstract

Humans are social beings and spend most of their time in groups. Group behavior is emergent, generated by members' personal characteristics and their interactions. It is therefore difficult to recognize in peer-to-peer (P2P) systems where the emergent behavior itself cannot be directly observed. We introduce 2 novel algorithms for distributed probabilistic inference (DPI) of group activities using loopy belief propagation (LBP). We evaluate their performance using an experiment in which 10 individuals play 6 team sports and show that these activities are emergent in nature through natural processes. Centralized recognition performs very well, upwards of an F-score of 0.95 for large window sizes. The distributed methods iteratively converge to solutions which are comparable to centralized methods. DPI-LBP also reduces energy consumption by a factor of 7 to 40, where a centralized unit or infrastructure is not required.

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Cited By

Huan RBo AShu JChen PLiang R(2023)Two-Domain Joint Attention Mechanism Based on Sensor Data for Group Activity RecognitionIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2023.324646972(1-15)Online publication date: 2023
https://doi.org/10.1109/TIM.2023.3246469
Kim KKim HLee D(2022)A Correlation-based Real-time Segmentation Scheme for Multi-user Collaborative Activities2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC54236.2022.00150(972-981)Online publication date: Jun-2022
https://doi.org/10.1109/COMPSAC54236.2022.00150
Meurisch CGogel ASchmidt BNolle TJanssen FSchweizer IMühlhäuser MGu TKotagiri RLiu H(2017)Capturing Daily Student Life by Recognizing Complex Activities Using SmartphonesProceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3144457.3144472(156-165)Online publication date: 7-Nov-2017
https://dl.acm.org/doi/10.1145/3144457.3144472
Show More Cited By

Index Terms

Group activity recognition using belief propagation for wearable devices
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence

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Information

Published In

cover image ACM Conferences

ISWC '14: Proceedings of the 2014 ACM International Symposium on Wearable Computers

September 2014

154 pages

ISBN:9781450329699

DOI:10.1145/2634317

General Chair:
Lucy Dunne
University of Minnesota Research
,
Program Chairs:
Tom Martin
Virginia Tech
,
Michael Beigl
Karlsruhe Institute of Technology, DE

Copyright © 2014 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 the author(s) 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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Publication History

Published: 13 September 2014

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UbiComp '14: The 2014 ACM Conference on Ubiquitous Computing

September 13 - 17, 2014

Washington, Seattle

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Overall Acceptance Rate 38 of 196 submissions, 19%

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Cited By

Huan RBo AShu JChen PLiang R(2023)Two-Domain Joint Attention Mechanism Based on Sensor Data for Group Activity RecognitionIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2023.324646972(1-15)Online publication date: 2023
https://doi.org/10.1109/TIM.2023.3246469
Kim KKim HLee D(2022)A Correlation-based Real-time Segmentation Scheme for Multi-user Collaborative Activities2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC54236.2022.00150(972-981)Online publication date: Jun-2022
https://doi.org/10.1109/COMPSAC54236.2022.00150
Meurisch CGogel ASchmidt BNolle TJanssen FSchweizer IMühlhäuser MGu TKotagiri RLiu H(2017)Capturing Daily Student Life by Recognizing Complex Activities Using SmartphonesProceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3144457.3144472(156-165)Online publication date: 7-Nov-2017
https://dl.acm.org/doi/10.1145/3144457.3144472
Ward JPirkl GHevesi PLukowicz P(2017)Detecting physical collaborations in a group task using body-worn microphones and accelerometers2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)10.1109/PERCOMW.2017.7917570(268-273)Online publication date: Mar-2017
https://doi.org/10.1109/PERCOMW.2017.7917570
Lee YKang SMin CJu YHwang ISong J(2016)CoMon+: A Cooperative Context Monitoring System for Multi-Device Personal Sensing EnvironmentsIEEE Transactions on Mobile Computing10.1109/TMC.2015.245290015:8(1908-1924)Online publication date: 1-Aug-2016
https://doi.org/10.1109/TMC.2015.2452900
Zhu ZBlanke UTröster G(2016)Recognizing composite daily activities from crowd-labelled social media dataPervasive and Mobile Computing10.1016/j.pmcj.2015.10.00726:C(103-120)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1016/j.pmcj.2015.10.007
Poon CLo BYuce MAlomainy AHao Y(2015)Body Sensor Networks: In the Era of Big Data and BeyondIEEE Reviews in Biomedical Engineering10.1109/RBME.2015.24272548(4-16)Online publication date: 2015
https://doi.org/10.1109/RBME.2015.2427254
Gordon DWirz MRoggen DTröster GBeigl MDunne LMartin TBeigl M(2014)Group affiliation detection using model divergence for wearable devicesProceedings of the 2014 ACM International Symposium on Wearable Computers10.1145/2634317.2634319(19-26)Online publication date: 13-Sep-2014
https://dl.acm.org/doi/10.1145/2634317.2634319

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