Learning Setting-Generalized Activity Models for Smart Spaces
Open accessAbstract
The data mining and pervasive computing technologies found in smart homes offer unprecedented opportunities for providing context-aware services, including health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to provide these services, smart environment algorithms need to recognize and track activities that people normally perform as part of their daily routines. However, activity recognition has typically involved gathering and labeling large amounts of data in each setting to learn a model for activities in that setting. We hypothesize that generalized models can be learned for common activities that span multiple environment settings and resident types. We describe our approach to learning these models and demonstrate the approach using eleven CASAS datasets collected in seven environments.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [33]
The ADL Recognition section identifies naïve Bayes classifiers and Markov models as common activity-recognition approaches, and Abstracting Activity Models reports recognition results using annotated activities. The paper does not discuss dynamic Bayes networks or establish that promising results require a large amount of labeled data; its Conclusions instead emphasize recognition in new settings with little or no training data. Page numbers are unavailable in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredDiscriminative approaches that model the boundary between different activity classes offer an effective alternative.
“On the other hand, discriminative approaches that model the boundary between different activity classes offer an effective alternative.”
From Forecasting occurrences of activities (2017) · cites this paper as [33]
Section 2 (ADL Recognition; page unavailable) presents conditional random fields as an activity-recognition alternative and describes how they assign activity labels. It does not describe discriminative approaches as modeling boundaries between activity classes. Section 3 reports recognition accuracy for the CRF alongside other models, supporting its use as an alternative but not that specific characterization. (LLM, conf=0.78)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Keeping the Resident in the Loop: Adapting the Smart Home to the User
Rashidi, P., & Cook, D. J. (2009) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/tsmca.2009.2025137 · Cited by 443 · see citing works
Self-citationAssessing the Quality of Activities in a Smart Environment
Schmitter‐Edgecombe, M., & Cook, D. J. (2009) · Methods of Information in Medicine · doi:10.3414/me0592 · Cited by 405 · see citing works
Self-citationWadley, V. G., Okonkwo, O., Crowe, M., & Ross-Meadows, L. A. (2008) · The American Journal of Geriatric Psychiatry · doi:10.1097/01.jgp.0000310780.04465.13 · Cited by 187 · see citing works
What Do Family Caregivers of Alzheimer’s Disease Patients Desire in Smart Home Technologies?
Ollivet, C., Guigui, C., Hervé, C., & Rialle, V. (2007) · Methods of Information in Medicine · doi:10.3414/me9102 · Cited by 139 · see citing works
Flagged first, then by citations
Research works printed but not linked: 7
- [4]Cook D Das S Smart Environments: Technology, Protocols, and Applications 2004 Wiley
- [5]Maurer U Smailagic A Siewiorek D Deisher M Activity recognition and monitoring using multiple sensors on different body positions 2004 99 102 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
- [6]Munguia-Tapia E Intille SS Larson K Activity recognition in the home using simple and ubiquitous sensors 2004 158 175 Proceedings of Pervasive
- [8]Liao L Fox D Kautz H Location-based activity recognition using relational Markov networks 2005 773 778 Proceedings of the International Joint Conference on Artificial Intelligence
- [9]Viterbi A Error bounds for convolutional codes and an asymptotically-optimum decoding algorithm IEEE Transactions on Information Theory 1967 13 2 260 269
- [10]McCallum A Efficiently inducing features of conditional random fields 2003 403 410 Proceedings of the Conference on Uncertainty in Artificial Intelligence
- [12]Yang Q Pan S Zheng V Estimating location using wi-fi IEEE Intelligent Systems 2008 23 1 8 13
Cited by
Sorted by citations
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-citationTransfer learning for activity recognition: a survey
Cook, D., Feuz, K. D., & Krishnan, N. C. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-013-0665-3 · Cited by 447 · see citing works
Self-citationAnalyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques
Cook, D. J., Schmitter-Edgecombe, M., & Dawadi, P. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2461659 · Cited by 123 · see citing works
Self-citationAlaghbari, K. A., Md. Saad, M. H., Hussain, A., & Alam, M. R. (2022) · IEEE Access · doi:10.1109/access.2022.3157726 · Cited by 61 · see citing works
“Several datasets such as CASAS [22] [24] and Kasteren [25] are provided for the public to test the ability of ML and DL classifiers to recognise human activities in smart homes.”
BackgroundMethodHuman 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
“These techniques include decision trees, meta classifiers based on boosting and bagging, support vector machines, and discriminative probabilistic graphical models such as conditional random fields [33,41-43].”
BackgroundMethodSelf-citationActivity-Aware Energy-Efficient Automation of Smart Buildings
Thomas, B., & Cook, D. (2016) · Energies · doi:10.3390/en9080624 · Cited by 42 · see citing works
Self-citationIntelligent Decision Support System for Dementia Care Through Smart Home
Gayathri, K. S., & Easwarakumar, K. S. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.07.281 · Cited by 22 · see citing works
Learning models for activity recognition in smart homes
Fahad, L. G., Ali, A., & Rajarajan, M. (2015) · Lecture Notes in Electrical Engineering · doi:10.1007/978-3-662-46578-3_97 · Cited by 16 · see citing works
Marcello, F., Pilloni, V., & Giusto, D. (2019) · Energies · doi:10.3390/en12132631 · Cited by 13 · see citing works
- Authors
- Cook, D.
- Affiliations
- Washington State University (US)
- Corresponding author
- Cook, Diane
- ORCID
- Cook, Diane
- Venue
- IEEE Intelligent Systems
- Published
- 2012-01
- Volume
- 27
- Issue
- 1
- Pages
- 32-38
- ISSN
- 1541-1672 (ISSN-L)
- PMC
- PMC3068197
- OpenAlex
- W2021361613
- Cited by
- 445
- References
- 4
- Citations by year
- 2026: 10 · 2025: 21 · 2024: 23 · 2023: 33 · 2022: 38 · 2021: 34 · 2020: 41 · 2019: 36 · 2018: 34 · 2017: 31 · 2016: 35 · 2015: 29 · 2014: 42 · 2013: 28 · 2012: 8
- FWCI
- 14.11
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Intelligent Systems (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation
- National Institute of Biomedical Imaging and Bioengineering (R01EB009675, R01EB015853)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
- Keywords
- activity recognition, home automation, context-aware services, health monitoring, activity monitoring, CASAS dataset, ubiquitous computing, daily routine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
