Recognizing independent and joint activities among multiple residents in smart environments
Open accessAbstract
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. A primary challenge that needs to be tackled to meet this need is the ability to recognize and track functional activities that people perform in their own homes and everyday settings. In this paper, we look at approaches to perform real-time recognition of Activities of Daily Living. We enhance other related research efforts to develop approaches that are effective when activities are interrupted and interleaved. To evaluate the accuracy of our recognition algorithms we assess them using real data collected from participants performing activities in our on-campus smart apartment testbed.
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Generative approaches such as naïve Bayes classifiers, Markov models and dynamic Bayes networks have yielded promising results for behavior modeling and offline activity recognition when a large amount of labeled data is available.
“Generative approaches such as naïve Bayes classifiers, Markov models and dynamic Bayes networks have yielded promising results for behavior modeling and offline activity recognition when a large amount of labeled data is available [33–40].”
From Forecasting occurrences of activities (2017) · cites this paper as [37]
In §3 (Probabilistic models), the paper evaluates a hidden Markov model on labeled activity data using threefold cross-validation and reports recognition accuracy above random guessing. In §5 (Conclusions), it calls Markov models effective but says labeled training data may not be readily available in realistic settings. It does not establish that a large amount of labeled data is required; this citation supports the Markov-model example, not the other methods named alongside it. (LLM, conf=0.84)
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Research works printed but not linked: 14
- Cook DJ Das SK 2004 Smart environments: technology, protocols, and applications 1st Wiley New York
- Cook DJ Schmitter-Edgecombe M 2009 Assessing the quality of activities in a smart environment Methods Inf Med in press PMC2759863 19448886
- Doctor F Hagras H Callaghan V 2005 A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments IEEE Trans Syst Man Cybern Part A 35 1 55 56
- Liao L Fox D Kautz H 2005 Location-based activity recognition using relational Markov networks Proceedings of the international joint conference on artificial intelligence 773 778
- Maurer U Smailagic A Siewiorek D Deisher M 2006 Activity recognition and monitoring using multiple sensors on different body positions Proceedings of the international workshop on wearable and implantable body sensor networks 113 116
- Philipose M Fishkin K Perkowitz M Patterson D Fox D Kautz H Hahnel D 2004 Inferring activities from interactions with objects IEEE Pervasive Comput 3 4 50 57
- Reisberg B Finkel S Overall J Schmidt-Gollas N Kanowski S Lehfeld H 2001 The Alzheimer's disease activities of daily living international scale (ASL-IS) Int Psychogeriatr 13 163 181 11495392
- Rialle V Ollivet C Guigui C Herve C 2008 What do family caregivers of Alzheimer's disease patients desire in smart home technologies? Methods Inf Med 47 63 69 18213430
- Sanchez D Tentori M Favela J 2008 Activity recognition for the smart hospital IEEE Intell Syst 23 2 50 57
- Schmitter-Edgecombe M Woo E Greeley D 2008 Memory deficits, everyday functioning, and mild cognitive impairment Proceedings of the annual rehabilitation psychology conference 19254090
- Singla G Cook D Schmitter-Edgecombe M 2009 Tracking activities in complex settings using smart environment technologies Int J BioSci Psychiatr Technol 1 1 25 35 PMC2794487 20019890
- Viterbi A 1967 Error bounds for convolutional codes and an asymptotically-optimum decoding algorithm IEEE Trans Inf Theory 13 2 260 269
- Wadley V Okonkwo O Crowe M Ross-Meadows LA 2007 Mild Cognitive Impairment and everyday function: Evidence of reduced speed in performing instrumental activities of daily living Am J Geriatr Psychiatry 16 5 416 424 18448852
- Wren C Munguia-Tapia E 2006 Toward scalable activity recognition for sensor networks Proceedings of the workshop on location and context-awareness 168 185
Cited by
Sorted by citations
Deep Learning for Sensor-based Human Activity Recognition
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2022) · ACM Computing Surveys · doi:10.1145/3447744 · Cited by 690 · see citing works
Activity recognition on streaming sensor data
Krishnan, N. C., & Cook, D. J. (2014) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2012.07.003 · Cited by 488 · see citing works
Self-citationDeep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2020) · arXiv · doi:10.48550/arxiv.2001.07416 · Cited by 314 · see citing works
Human Activity Recognition Data Analysis: History, Evolutions, and New Trends
Ariza-Colpas, P. P., Vicario, E., Oviedo-Carrascal, A. I., Butt Aziz, S., Piñeres-Melo, M. A., Quintero-Linero, A., & Patara, F. (2022) · Sensors · doi:10.3390/s22093401 · Cited by 52 · see citing works
Forecasting occurrences of activities
Minor, B., & Cook, D. J. (2017) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2016.09.010 · Cited by 45 · see citing works
“Generative approaches such as naïve Bayes classifiers, Markov models and dynamic Bayes networks have yielded promising results for behavior modeling and offline activity recognition when a large amount of labeled data is available [33-40].”
BackgroundSelf-citationAutomated activity-aware prompting for activity initiation
Holder, L. B., & Cook, D. J. (2013) · Gerontechnology · doi:10.4017/gt.2013.11.4.005.00 · Cited by 24 · see citing works
Self-citation
- Authors
- Singla, G., Cook, D. J., & Schmitter-Edgecombe, M.
- Affiliations
- Washington State University (US)
- Corresponding author
- Cook, Diane J.
- ORCID
- Cook, Diane J.
- Venue
- Journal of Ambient Intelligence and Humanized Computing
- Published
- 2009-12-01
- Volume
- 1
- Issue
- 1
- Pages
- 57-63
- ISSN
- 1868-5137 (ISSN-L)
- 1868-5145
- PMC
- PMC2958106
- OpenAlex
- W2004099281
- Cited by
- 265
- References
- 0
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 13 · 2023: 10 · 2022: 19 · 2021: 12 · 2020: 23 · 2019: 24 · 2018: 18 · 2017: 26 · 2016: 16 · 2015: 21 · 2014: 25 · 2013: 17 · 2012: 12
- FWCI
- 12.51
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Ambient Intelligence and Humanized Computing (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Drug Abuse (R21DA024294)
- National Institute of Biomedical Imaging and Bioengineering (R01EB009675)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
- Keywords
- activities of daily living, activity recognition, home automation, pervasive sensing, independent living, health monitoring, real-time recognition, ambient intelligence
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