research-article

Automatic Extraction of Behavioral Patterns for Elderly Mobility and Daily Routine Analysis

Authors:

William K. Cheung,

Joseph K. NgAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 9, Issue 5

Article No.: 54, Pages 1 - 26

https://doi.org/10.1145/3178116

Published: 01 June 2018 Publication History

Abstract

The elderly living in smart homes can have their daily movement recorded and analyzed. As different elders can have their own living habits, a methodology that can automatically identify their daily activities and discover their daily routines will be useful for better elderly care and support. In this article, we focus on automatic detection of behavioral patterns from the trajectory data of an individual for activity identification as well as daily routine discovery. The underlying challenges lie in the need to consider longer-range dependency of the sensor triggering events and spatiotemporal variations of the behavioral patterns exhibited by humans. We propose to represent the trajectory data using a behavior-aware flow graph that is a probabilistic finite state automaton with its nodes and edges attributed with some local behavior-aware features. We identify the underlying subflows as the behavioral patterns using the kernel k-means algorithm. Given the identified activities, we propose a novel nominal matrix factorization method under a Bayesian framework with Lasso to extract highly interpretable daily routines. For empirical evaluation, the proposed methodology has been compared with a number of existing methods based on both synthetic and publicly available real smart home datasets with promising results obtained. We also discuss how the proposed unsupervised methodology can be used to support exploratory behavior analysis for elderly care.

References

[1]

Radim Belohlavek and Marketa Krmelova. 2013. Beyond boolean matrix decompositions: Toward factor analysis and dimensionality reduction of ordinal data. In Proceedings of the IEEE 13th International Conference on Data Mining. IEEE, 961--966.

[2]

Oliver Brdiczka, James L. Crowley, and Patrick Reignier. 2009. Learning situation models in a smart home. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39, 1 (2009), 56--63.

Digital Library

[3]

Deng Cai, Xiaofei He, Jiawei Han, and Thomas S. Huang. 2011. Graph regularized non-negative matrix factorization for data representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 8 (2011), 1548--1560.

Digital Library

[4]

Rafael C. Carrasco and José Oncina. 1994. Learning stochastic regular grammars by means of a state merging method. In International Colloquium on Grammatical Inference. Springer, 139--152.

Digital Library

[5]

Diane J. Cook. 2012. Learning setting-generalized activity models for smart spaces. IEEE Intelligent Systems 27, 1 (2012), 32--38.

Digital Library

[6]

Morris H. DeGroot. 2005. Optimal Statistical Decisions. Vol. 82. John Wiley 8 Sons.

[7]

Inderjit S. Dhillon, Yuqiang Guan, and Brian Kulis. 2007. Weighted graph cuts without eigenvectors a multilevel approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 11 (2007), 1944--1957.

Digital Library

[8]

Charalampos Doukas, Ilias Maglogiannis, Philippos Tragas, Dimitris Liapis, and Gregory Yovanof. 2007. Patient fall detection using support vector machines. In Proceedings of the IFIP International Conference on Artificial Intelligence Applications and Innovations. Springer, 147--156.

[9]

Nathan Eagle and Alex Sandy Pentland. 2009. Eigenbehaviors: Identifying structure in routine. Behavioral Ecology and Sociobiology 63, 7 (2009), 1057--1066.

[10]

Mustafa Fanaswala and Vikram Krishnamurthy. 2013. Detection of anomalous trajectory patterns in target tracking via stochastic context-free grammars and reciprocal process models. IEEE Journal of Selected Topics in Signal Processing 7, 1 (2013), 76--90.

[11]

Nicolas Gillis and Stephen A. Vavasis. 2014. Fast and robust recursive algorithms for separable nonnegative matrix factorization. IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 4 (2014), 698--714.

Digital Library

[12]

Marta C. Gonzalez, Cesar A. Hidalgo, and Albert-Laszlo Barabasi. 2008. Understanding individual human mobility patterns. Nature 453, 7196 (June 2008), 779--782.

[13]

Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 855--864.

Digital Library

[14]

Pengyu Hong, Matthew Turk, and Thomas S. Huang. 2000. Constructing finite state machines for fast gesture recognition. In Proceedings of the 15th International Conference on Pattern Recognition, Vol. 3. IEEE, 691--694.

Digital Library

[15]

Kejun Huang, Nicholas D. Sidiropoulos, and Ananthram Swami. 2014. Non-negative matrix factorization revisited: Uniqueness and algorithm for symmetric decomposition. IEEE Transactions on Signal Processing 62, 1 (2014), 211--224.

Digital Library

[16]

Till Hülnhagen, Ingo Dengler, Andreas Tamke, Thao Dang, and Gabi Breuel. 2010. Maneuver recognition using probabilistic finite-state machines and fuzzy logic. In 2010 IEEE Intelligent Vehicles Symposium (IV). IEEE, 65--70.

[17]

Shan Jiang, Joseph Ferreira, and Marta C. González. 2012. Clustering daily patterns of human activities in the city. Data Mining and Knowledge Discovery 25, 3 (2012), 478--510.

[18]

Liping Jing, Peng Wang, and Liu Yang. 2015. Sparse probabilistic matrix factorization by Laplace distribution for collaborative filtering. In Proceedings of the 24th International Conference on Artificial Intelligence. 1771--1777.

Digital Library

[19]

Hilde Kuehne, Ali Arslan, and Thomas Serre. 2014. The language of actions: Recovering the syntax and semantics of goal-directed human activities. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 780--787.

Digital Library

[20]

Jennifer R. Kwapisz, Gary M. Weiss, and Samuel A. Moore. 2011. Activity recognition using cell phone accelerometers. ACM SigKDD Explorations Newsletter 12, 2 (2011), 74--82.

Digital Library

[21]

Daniel D. Lee and H. Sebastian Seung. 1999. Learning the parts of objects by nonnegative matrix factorization. Nature 401 (1999), 788--791.

[22]

Matthew L. Lee and Anind K. Dey. 2011. Reflecting on pills and phone use: Supporting awareness of functional abilities for older adults. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2095--2104.

Digital Library

[23]

Kang Li and Yun Fu. 2014. Prediction of human activity by discovering temporal sequence patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 8 (2014), 1644--1657.

Digital Library

[24]

Zhao Li, Xindong Wu, and Hong Peng. 2010. Nonnegative matrix factorization on orthogonal subspace. Pattern Recognition Letters 31, 9 (2010), 905--911.

Digital Library

[25]

Matthew M. Lin, Bo Dong, and Moody T. Chu. 2005. Integer Matrix Factorization and Its Application. Technical Report.

[26]

Qiang Lin, Daqing Zhang, Dongsheng Li, Hongbo Ni, and Xingshe Zhou. 2013. Extracting intra-and inter-activity association patterns from daily routines of elders. In Proceedings of International Conference on Smart Homes and Health Telematics. Springer, 36--44.

[27]

Samuel Maurus and Claudia Plant. 2014. Ternary matrix factorization. In Proceedings of the IEEE International Conference on Data Mining. IEEE, 400--409.

Digital Library

[28]

Pauli Miettinen. 2012. Dynamic boolean matrix factorizations. In Proceedings of the 12th IEEE International Conference on Data Mining. IEEE, 519--528.

Digital Library

[29]

Ehsan Nazerfard, Parisa Rashidi, and Diane J. Cook. 2010. Discovering temporal features and relations of activity patterns. In Proceedings of the IEEE International Conference on Data Mining Workshops. IEEE, 1069--1075.

Digital Library

[30]

Ulrich Paquet, Blaise Thomson, and Ole Winther. 2012. A hierarchical model for ordinal matrix factorization. Statistics and Computing 22, 4 (2012), 945--957.

Digital Library

[31]

Christine Parent, Stefano Spaccapietra, Chiara Renso, Gennady Andrienko, Natalia Andrienko, Vania Bogorny, Maria Luisa Damiani, Aris Gkoulalas-Divanis, Jose Macedo, Nikos Pelekis, Yannis Theodoridis, and Zhixian Yan. 2013. Semantic trajectories modeling and analysis. Computing Surveys 45, 4 (2013), 42.

Digital Library

[32]

Kyungseo Park, Yong Lin, Vangelis Metsis, Zhengyi Le, and Fillia Makedon. 2010. Abnormal human behavioral pattern detection in assisted living environments. In Proceedings of the 3rd International Conference on Pervasive Technologies Related to Assistive Environments. ACM, 9.

Digital Library

[33]

Trevor Park and George Casella. 2008. The bayesian lasso. Journal of the American Statistical Association 103, 482 (2008), 681--686.

[34]

Katerina Pastra and Yiannis Aloimonos. 2012. The minimalist grammar of action. Philosophical Transactions of the Royal Society of London B: Biological Sciences 367, 1585 (2012), 103--117.

[35]

Huan-Kai Peng, Pang Wu, Jiang Zhu, and Joy Ying Zhang. 2011. Helix: Unsupervised grammar induction for structured activity recognition. In Proceedings of the IEEE 11th International Conference on Data Mining. IEEE, 1194--1199.

Digital Library

[36]

Hamed Pirsiavash and Deva Ramanan. 2014. Parsing videos of actions with segmental grammars. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 612--619.

Digital Library

[37]

Thor Prentow, Andreas Thom, Henrik Blunck, and Jan Vahrenhold. 2015. Making sense of trajectory data in indoor spaces. In Proceedings of the 16th IEEE International Conference on Mobile Data Management (MDM), Vol. 1. IEEE, 116--121.

Digital Library

[38]

Parisa Rashidi, Diane J. Cook, Lawrence B. Holder, and Maureen Schmitter-Edgecombe. 2011. Discovering activities to recognize and track in a smart environment. IEEE Transactions on Knowledge and Data Engineering 23, 4 (2011), 527--539.

Digital Library

[39]

Jérémie Saives, Clément Pianon, and Gregory Faraut. 2015. Activity discovery and detection of behavioral deviations of an inhabitant from binary sensors. IEEE Transactions on Automation Science and Engineering 12, 4 (2015), 1211--1224.

[40]

Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems, Vol. 20. 1257--1264.

Digital Library

[41]

Mikkel N. Schmidt, Ole Winther, and Lars Kai Hansen. 2009. Bayesian non-negative matrix factorization. In Proceedings of International Conference on Independent Component Analysis and Signal Separation. 540--547.

Digital Library

[42]

Christian M. Schneider, Vitaly Belik, Thomas Couronné, Zbigniew Smoreda, and Marta C. González. 2013. Unravelling daily human mobility motifs. Journal of the Royal Society Interface 10, 84 (2013), 20130246.

[43]

Chaoming Song, Zehui Qu, Nicholas Blumm, and Albert-Lszl Barabsi. 2010. Limits of predictability in human mobility. Science 327, 5968 (2010), 1018--1021.

[44]

Feng-Tso Sun, Yi-Ting Yeh, Heng-Tze Cheng, Cynthia Kuo, and Martin Griss. 2014. Nonparametric discovery of human routines from sensor data. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom’14). IEEE, 11--19.

[45]

David Tacconi, Oscar Mayora, Paul Lukowicz, Bert Arnrich, Cornelia Setz, Gerhard Troster, and Christian Haring. 2008. Activity and emotion recognition to support early diagnosis of psychiatric diseases. In Proceedings of the 2nd International Conference on Pervasive Computing Technologies for Healthcare (Pervasive Health’08). IEEE, 100--102.

[46]

Franck Thollard, Pierre Dupont, and Colin de la Higuera. 2000. Probabilistic DFA inference using Kullback-Leibler divergence and minimality. In Proceedings of the 17th International Conference on Machine Learning. 975--982.

Digital Library

[47]

Robert Tibshirani. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) 58 (1996), 267--288.

[48]

Enrique Vidal, Franck Thollard, Colin De La Higuera, Francisco Casacuberta, and Rafael C. Carrasco. 2005. Probabilistic finite-state machines part I. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 7 (2005), 1013--1025.

Digital Library

[49]

Xiangye Xiao, Yu Zheng, Qiong Luo, and Xing Xie. 2014. Inferring social ties between users with human location history. Journal of Ambient Intelligence and Humanized Computing 5, 1 (2014), 3--19.

[50]

Minjie Xu, Jun Zhu, and Bo Zhang. 2012. Nonparametric max-margin matrix factorization for collaborative prediction. In Advances in Neural Information Processing Systems, Vol. 12. 64--72.

Digital Library

[51]

Zhixian Yan, Dipanjan Chakraborty, Christine Parent, Stefano Spaccapietra, and Karl Aberer. 2013. Semantic trajectories: Mobility data computation and annotation. ACM Transactions on Intelligent Systems and Technology 4, 3 (2013), 49.

Digital Library

[52]

Nicholas Jing Yuan, Yu Zheng, Xing Xie, Yingzi Wang, Kai Zheng, and Hui Xiong. 2015. Discovering urban functional zones using latent activity trajectories. IEEE Transactions on Knowledge and Data Engineering 27, 3 (2015), 712--725.

Digital Library

[53]

Tingzhi Zhao, Hongbo Ni, Xingshe Zhou, Lin Qiang, Daqing Zhang, and Zhiwen Yu. 2014. Detecting abnormal patterns of daily activities for the elderly living alone. In Proceedings of the International Conference on Health Information Science. Springer, 95--108.

[54]

Jiangchuan Zheng, Siyuan Liu, and Lionel M. Ni. 2013. Effective routine behavior pattern discovery from sparse mobile phone data via collaborative filtering. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications. 29--37.

[55]

Kai Zheng, Yu Zheng, Nicholas J. Yuan, Shuo Shang, and Xiaofang Zhou. 2014. Online discovery of gathering patterns over trajectories. IEEE Transactions on Knowledge and Data Engineering 26, 8 (2014), 1974--1988.

Cited By

Chifu VPop CRancea AMorar ACioara TAntal MAnghel I(2022)Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities PatternsSensors10.3390/s2213480322:13(4803)Online publication date: 25-Jun-2022
https://doi.org/10.3390/s22134803
Chifu VPop CDemjen DSocaci RTodea DAntal MCioara TAnghel IAntal C(2022)Identifying and Monitoring the Daily Routine of Seniors Living at HomeSensors10.3390/s2203099222:3(992)Online publication date: 27-Jan-2022
https://doi.org/10.3390/s22030992
Tan YKoh SGan CTan S(2022)Home Activities Sequence Pattern AnalysisProceedings of the 8th International Conference on Computational Science and Technology10.1007/978-981-16-8515-6_36(459-467)Online publication date: 26-Mar-2022
https://doi.org/10.1007/978-981-16-8515-6_36
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Automatic Extraction of Behavioral Patterns for Elderly Mobility and Daily Routine Analysis

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 9, Issue 5

Research Survey and Regular Papers

September 2018

274 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3210369

Issue’s Table of Contents

Copyright © 2018 ACM.

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Publication History

Published: 01 June 2018

Accepted: 01 January 2018

Revised: 01 November 2017

Received: 01 March 2017

Published in TIST Volume 9, Issue 5

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Cited By

Chifu VPop CRancea AMorar ACioara TAntal MAnghel I(2022)Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities PatternsSensors10.3390/s2213480322:13(4803)Online publication date: 25-Jun-2022
https://doi.org/10.3390/s22134803
Chifu VPop CDemjen DSocaci RTodea DAntal MCioara TAnghel IAntal C(2022)Identifying and Monitoring the Daily Routine of Seniors Living at HomeSensors10.3390/s2203099222:3(992)Online publication date: 27-Jan-2022
https://doi.org/10.3390/s22030992
Tan YKoh SGan CTan S(2022)Home Activities Sequence Pattern AnalysisProceedings of the 8th International Conference on Computational Science and Technology10.1007/978-981-16-8515-6_36(459-467)Online publication date: 26-Mar-2022
https://doi.org/10.1007/978-981-16-8515-6_36
Lin BCook DSchmitter-Edgecombe M(2020)Using continuous sensor data to formalize a model of in-home activity patternsJournal of Ambient Intelligence and Smart Environments10.3233/AIS-20056212:3(183-201)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/AIS-200562
Petroșanu DCăruțașu GCăruțașu NPîrjan A(2019)A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building ManagementEnergies10.3390/en1224474512:24(4745)Online publication date: 12-Dec-2019
https://doi.org/10.3390/en12244745
Parvin PChessa SKaptein MPaternò F(2019)Personalized real-time anomaly detection and health feedback for older adultsJournal of Ambient Intelligence and Smart Environments10.3233/AIS-19053611:5(453-469)Online publication date: 12-Sep-2019
https://doi.org/10.3233/AIS-190536
Maharjan RShiraishi KYamamoto TYamamoto YOhshima H(2019)Development of IoT Monitoring Device and Prediction of Daily Life BehaviorProceedings of the 21st International Conference on Information Integration and Web-based Applications & Services10.1145/3366030.3366123(584-588)Online publication date: 2-Dec-2019
https://dl.acm.org/doi/10.1145/3366030.3366123
Varlamis ISardianos CBouras G(2019)Mining Habitual User Choices from Google Maps History LogsPutting Social Media and Networking Data in Practice for Education, Planning, Prediction and Recommendation10.1007/978-3-030-33698-1_9(151-175)Online publication date: 28-Dec-2019
https://doi.org/10.1007/978-3-030-33698-1_9

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