research-article

Group affiliation detection using model divergence for wearable devices

Authors:

Gerhard Tröster,

Michael BeiglAuthors Info & Claims

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

Pages 19 - 26

https://doi.org/10.1145/2634317.2634319

Published: 13 September 2014 Publication History

Abstract

Methods for recognizing group affiliations using mobile devices have been proposed using centralized instances to aggregate and evaluate data. However centralized systems do not scale well and fail when the network is congested. We present a method for distributed, peer-to-peer (P2P) recognition of group affiliations in multi-group environments, using the divergence of mobile phone sensor data distributions as an indicator of similarity. The method assesses pairwise similarity between individuals using model parameters instead of sensor observations, and then interprets that information in a distributed manner. An experiment was conducted with 10 individuals in different group configurations to compare P2P and conventional centralized approaches. Although the output of the proposed method fluctuates, we can still correctly detect 93% of group affiliations by applying a filter. We foresee applications in mobile social networking, life logging, smart environments, crowd situations and possibly crowd emergencies.

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Cited By

Arrotta LCivitarese GChen XCumin JBettini C(2025)Multi-subject human activities: A survey of recognition and evaluation methods based on a formal frameworkExpert Systems with Applications10.1016/j.eswa.2024.126178267(126178)Online publication date: Apr-2025
https://doi.org/10.1016/j.eswa.2024.126178
Sun YGreaves DOrgs Gde C. Hamilton ADay SWard J(2023)Using Wearable Sensors to Measure Interpersonal Synchrony in Actors and Audience Members During a Live Theatre PerformanceProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35807817:1(1-29)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3580781
Jiang GLi MLiu XLiu WJia YJiang HLei JXiao FZhang K(2023)WiDE: WiFi Distance Based Group Profiling Via Machine LearningIEEE Transactions on Mobile Computing10.1109/TMC.2021.307384822:1(607-620)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TMC.2021.3073848
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Index Terms

Group affiliation detection using model divergence for wearable devices
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence

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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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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

Arrotta LCivitarese GChen XCumin JBettini C(2025)Multi-subject human activities: A survey of recognition and evaluation methods based on a formal frameworkExpert Systems with Applications10.1016/j.eswa.2024.126178267(126178)Online publication date: Apr-2025
https://doi.org/10.1016/j.eswa.2024.126178
Sun YGreaves DOrgs Gde C. Hamilton ADay SWard J(2023)Using Wearable Sensors to Measure Interpersonal Synchrony in Actors and Audience Members During a Live Theatre PerformanceProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35807817:1(1-29)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3580781
Jiang GLi MLiu XLiu WJia YJiang HLei JXiao FZhang K(2023)WiDE: WiFi Distance Based Group Profiling Via Machine LearningIEEE Transactions on Mobile Computing10.1109/TMC.2021.307384822:1(607-620)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TMC.2021.3073848
Zhu WChen JXu LCao J(2022)Recognition of interactive human groups from mobile sensing dataComputer Communications10.1016/j.comcom.2022.04.028191:C(208-216)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.comcom.2022.04.028
Zhu WXu LTang YXie R(2021)Synchronized Data Collection for Human Group RecognitionSensors10.3390/s2121709421:21(7094)Online publication date: 26-Oct-2021
https://doi.org/10.3390/s21217094
Gravina RFortino G(2021)Wearable Body Sensor Networks: State-of-the-Art and Research DirectionsIEEE Sensors Journal10.1109/JSEN.2020.304444721:11(12511-12522)Online publication date: 1-Jun-2021
https://doi.org/10.1109/JSEN.2020.3044447
Abdel-Basset MChang VHawash HChakrabortty RRyan M(2021)Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary surveyAnnals of Operations Research10.1007/s10479-021-04164-3339:1-2(3-51)Online publication date: 8-Jul-2021
https://doi.org/10.1007/s10479-021-04164-3
Yu ZWang ZYu ZWang Z(2020)Group Behavior RecognitionHuman Behavior Analysis: Sensing and Understanding10.1007/978-981-15-2109-6_6(139-218)Online publication date: 1-Mar-2020
https://doi.org/10.1007/978-981-15-2109-6_6
Issa TDuan YIssa TChang V(2019)The Attitudes of Chinese Organizations Towards Cloud ComputingCloud Security10.4018/978-1-5225-8176-5.ch079(1577-1597)Online publication date: 2019
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Abkenar ALoke SZaslavsky ARahayu W(2019)GroupSenseACM Transactions on Embedded Computing Systems10.1145/329574717:6(1-26)Online publication date: 9-Jan-2019
https://dl.acm.org/doi/10.1145/3295747
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