research-article

A hybrid unsupervised/supervised model for group activity recognition

Authors:

Tomoya Hirano,

Takuya MaekawaAuthors Info & Claims

ISWC '13: Proceedings of the 2013 International Symposium on Wearable Computers

Pages 21 - 24

https://doi.org/10.1145/2493988.2494348

Published: 08 September 2013 Publication History

Get Access

Abstract

The new method proposed here recognizes activities performed by a group of users (e.g., attending a meeting, playing sports, and participating in a party) by using sensor data obtained from the users. Note that such group activities (GAs) have characteristics that differ from those of single user activities. For example, the number of users who participate in a GA is different for each activity. The number of meeting participants, for instance, may sometimes be different for each meeting. Also, a user may play different roles (e.g., `moderator' and `presenter' roles) in meetings on different days. We introduce the notion of role into our GA recognition model and try to capture the intrinsic characteristics of GAs with a hybrid unsupervised/supervised approach.

References

[1]

Cowling, M. Non-speech environmental sound recognition system for autonomous surveillance. PhD thesis, Griffith University, 2004.

Google Scholar

[2]

Dempster, A., Laird, N., Rubin, D., et al. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39, 1 (1977), 1--38.

Crossref

Google Scholar

[3]

Gordon, D., Hanne, J., Berchtold, M., Miyaki, T., and Beigl, M. Recognizing group activities using wearable sensors. MobiQuitous 2012 (2012), 350--361.

Google Scholar

[4]

Gu, T., Wu, Z., Wang, L., Tao, X., and Lu, J. Mining emerging patterns for recognizing activities of multiple users in pervasive computing. In MobiQuitous 2009 (2009), 1--10.

Google Scholar

[5]

Maekawa, T., and Watanabe, S. Unsupervised activity recognition with user's physical characteristics data. In Int'l Symp. on Wearable Computers (2011), 89--96.

Digital Library

Google Scholar

[6]

Rabiner, L. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77, 2 (1989), 257--286.

Crossref

Google Scholar

[7]

Wirz, M., Roggen, D., and Tröster, G. A methodology towards the detection of collective behavior patterns by means of body-worn sensors. In UbiLarge Workshop at Pervasive 2010 (2010).

Google Scholar

[8]

Zhang, D., Gatica-Perez, D., Bengio, S., and McCowan, I. Modeling individual and group actions in meetings with layered hmms. IEEE Transactions on Multimedia 8, 3 (2006), 509--520.

Digital Library

Google Scholar

Cited By

View all

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
Agarwal IYadav PGupta NYadav S(2021)Urban Sound Classification Using Machine Learning and Neural NetworksProceedings of 6th International Conference on Recent Trends in Computing10.1007/978-981-33-4501-0_31(323-330)Online publication date: 21-Apr-2021
https://doi.org/10.1007/978-981-33-4501-0_31
Chen HKim T(2020)Markov Logic Network-Based Group Activity Recognition in Smart BuildingsSmart and Sustainable Cities and Buildings10.1007/978-3-030-37635-2_32(459-467)Online publication date: 12-May-2020
https://doi.org/10.1007/978-3-030-37635-2_32
Show More Cited By

Index Terms

A hybrid unsupervised/supervised model for group activity recognition
1. Information systems
  1. Information retrieval
  2. Information storage systems

Recommendations

Activity recognition with hand-worn magnetic sensors

Activity recognition is a key technology for realizing ambient assisted living applications such as care of the elderly and home automation. This paper proposes a new activity recognition method that employs hand-worn magnetic sensors to recognize a ...
Annotating smart environment sensor data for activity learning
Smart Environments: Technology to Support Healthcare

The pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of ...
Activity Recognition based on High-Level Reasoning
ICPRAM 2016: Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods

In the context of Ambient Assisted Living (AAL), the detection of daily activities is an active field of research. In this study, we present an algorithm for the performed Activities of Daily Living (ADLs) related to personal hygiene, which is based on ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

ISWC '13: Proceedings of the 2013 International Symposium on Wearable Computers

September 2013

160 pages

ISBN:9781450321273

DOI:10.1145/2493988

General Chair:
Kristof Van Laerhoven
TU Darmstadt, Germany
,
Program Chairs:
Daniel Roggen
Newcastle University, UK
,
Daniel Gatica-Perez
Idiap and EPFL, Switzerland
,
Masaaki Fukumoto
Microsoft Research Asia, China

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].

In-Cooperation

SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 September 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

UbiComp '13

Sponsor:

SIGMOBILE
University of Florida
SIGCHI

UbiComp '13: The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 8 - 12, 2013

Zurich, Switzerland

Acceptance Rates

ISWC '13 Paper Acceptance Rate 20 of 101 submissions, 20%;

Overall Acceptance Rate 38 of 196 submissions, 19%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
391
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

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
Agarwal IYadav PGupta NYadav S(2021)Urban Sound Classification Using Machine Learning and Neural NetworksProceedings of 6th International Conference on Recent Trends in Computing10.1007/978-981-33-4501-0_31(323-330)Online publication date: 21-Apr-2021
https://doi.org/10.1007/978-981-33-4501-0_31
Chen HKim T(2020)Markov Logic Network-Based Group Activity Recognition in Smart BuildingsSmart and Sustainable Cities and Buildings10.1007/978-3-030-37635-2_32(459-467)Online publication date: 12-May-2020
https://doi.org/10.1007/978-3-030-37635-2_32
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
Nakatani TMaekawa TShirakawa MHara T(2018)Estimating the Physical Distance between Two Locations with Wi-Fi Received Signal Strength Information Using Obstacle-aware ApproachProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32649402:3(1-26)Online publication date: 18-Sep-2018
https://dl.acm.org/doi/10.1145/3264940
KIM YLEE Y(2017)Next-Activity Set Prediction Based on Sequence Partitioning to Reduce Activity Pattern Complexity in the Multi-User Smart SpaceIEICE Transactions on Information and Systems10.1587/transinf.2017EDP7056E100.D:10(2587-2596)Online publication date: 2017
https://doi.org/10.1587/transinf.2017EDP7056
Thomas BCook D(2016)Activity-Aware Energy-Efficient Automation of Smart BuildingsEnergies10.3390/en90806249:8(624)Online publication date: 9-Aug-2016
https://doi.org/10.3390/en9080624
Luitel BMurthy YKoolagudi S(2016)Sound event detection in urban soundscape using two-level classification2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)10.1109/DISCOVER.2016.7806268(259-263)Online publication date: Aug-2016
https://doi.org/10.1109/DISCOVER.2016.7806268
Abkenar ALoke SRahayu WZaslavsky A(2016)Energy Considerations for Continuous Group Activity Recognition Using Mobile Devices: The Case of GroupSense2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)10.1109/AINA.2016.94(479-486)Online publication date: Mar-2016
https://doi.org/10.1109/AINA.2016.94
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
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Activity recognition with hand-worn magnetic sensors

Annotating smart environment sensor data for activity learning

Activity Recognition based on High-Level Reasoning