The DOI matches its Crossref record; 24 of the 25 references it cites were found in a registry or an index.
Keeping the Resident in the Loop: Adapting the Smart Home to the User
Abstract
Advancements in supporting fields have increased the likelihood that smart-home technologies will become part of our everyday environments. However, many of these technologies are brittle and do not adapt to the user's explicit or implicit wishes. Here, we introduce CASAS, an adaptive smart-home system that utilizes machine learning techniques to discover patterns in resident's daily activities and to generate automation polices that mimic these patterns. Our approach does not make any assumptions about the activity structure or other underlying model parameters but leaves it completely to our algorithms to discover the smart-home resident's patterns. Another important aspect of CASAS is that it can adapt to changes in the discovered patterns based on the resident implicit and explicit feedback and can automatically update its model to reflect the changes. In this paper, we provide a description of the CASAS technologies and the results of experiments performed on both synthetic and real-world data.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“ML can help these systems dynamically adapt to changes in occupancy patterns [669].”
From Tackling climate change with machine learning (2023) · cites this paper as [669]
Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Introduction to Reinforcement Learning
Sutton, R. S., & Barto, A. G. (1998) · The MIT Press eBooks · Cited by 6,855
Agrawal, R., & Srikant, R. (2002) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.1995.380415 · Cited by 5,150
Activity Recognition in the Home Using Simple and Ubiquitous Sensors
Tapia, E. M., Intille, S. S., & Larson, K. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24646-6_10 · Cited by 1,323
The Gator Tech Smart House: a programmable pervasive space
Helal, S., Mann, W., Elzabadani, H., King, J., Kaddoura, Y., & Jansen, E. (2005) · Computer · doi:10.1109/mc.2005.107 · Cited by 891
The Neural Network House: An Environment that Adapts to its Inhabitants
Mozer, M. C. (1998) · Cited by 500
A Survey of Temporal Knowledge Discovery Paradigms and Methods
Roddick, J., & Spiliopoulou, M. (2002) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2002.1019212 · Cited by 491
Fawcett, T. E., & Provost, F. J. (1999) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312195 · Cited by 429
The human experience [of ubiquitous computing]
Abowd, G. D., Mynatt, E. D., & Rodden, T. A. (2002) · IEEE Pervasive Computing · doi:10.1109/mprv.2002.993144 · Cited by 353
Flagged first, then by citations
1 of the 25 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 401–434 of 434
Worldwide Research Trends on Smart Homes
Salmerón-Manzano, E., Rahmani‐Andebili, M., Alcayde, A., & Manzano-Agugliaro, F. R. (2021) · Power systems · doi:10.1007/978-3-030-64915-9_1
Multi-Channel Deep Networks on Sequence Data for Multi-Action Recognition
Qi, Q., Zhong, S., Wang, Y., & Long, J. (2020) · International Conference on Intelligent Computation Technology and Automation · doi:10.1109/icicta51737.2020.00087
(2014) · Elsevier eBooks · doi:10.1016/b978-0-12-398532-3.00022-1
Basic Knowledge, Strategies, and Procedures
Bock, T. A., Linner, T., Güttler, J., & Iturralde, K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139872034.002
Bock, T. A., Linner, T., Güttler, J., & Iturralde, K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139872034.007
Bock, T. A., Linner, T., Güttler, J., & Iturralde, K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139872034.004
Research and Development Projects for AAL Systems
Bock, T. A., Linner, T., Güttler, J., & Iturralde, K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139872034.005
Built Environment Upgrading for AAL (BeuAAL)
Bock, T. A., Linner, T., Güttler, J., & Iturralde, K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139872034.003
Bock, T. A., Linner, T., Güttler, J., & Iturralde, K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139872034.008
Application‐Oriented Learning Automata
Zhang, J., & Zhou, M. (2023) · Wiley eBooks · doi:10.1002/9781394188536.ch4
Offermann, J., Maidhof, C., & Ziefle, M. (2023) · Universal Access in the Information Society · doi:10.1007/s10209-023-01070-2
Enhancing Human Activity Recognition with IMU-based Motion Analysis
Singh, A., Verma, A., & Cherukuri, B. (2023) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n60023.2023.10541396
Wearable IMUs: Advancing Human Motion Analysis with Deep Learning
Das, S., Das, D., & Parashar, A. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-2671-4_29
De, D. K., Samal, S. R., Choudhry, A., & Mohapatra, H. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-76286-4_6
Kaithal, P. K., Sengar, A. S., Chourey, A., Gupta, C., Pandey, A., & Gangrade, S. (2025) · World Skills Conference on Universal Data Analytics and Sciences (WorldSUAS) · doi:10.1109/worldsuas66815.2025.11199300
Explainable Multimodal Fall Detection via Ontology-Guided Sensor Fusion and Semantic ReasoningPreprint
Pham, T. T. T., & Bui, C. T. (2025) · Research Square · doi:10.21203/rs.3.rs-8242584/v1
Clarkston, E., Chang, Y., & Voicu, R. C. (2026) · IEEE SoutheastCon · doi:10.1109/southeastcon63549.2026.11475948
A Review on AI‐Driven Energy Consumption Forecasting for Smart Buildings
Munir, S., Khan, M. T., Mazhar, T., Shahzad, T., Khan, M. A., Saeed, M. M. A., & Hamam, H. (2026) · IET Smart Grid · doi:10.1049/stg2.70082
A Multi-Modal Deep Learning Framework for Smart Nutrition Monitoring and Dietary Management
Chandra, M. S., Abijith, G., HarshVardhan, K., Kumar, E. R., & Irshadulla, M. (2026) · International Conference on Smart Multidomain Integrated Learning Environments (ICSMILE) · doi:10.1109/icsmile69273.2026.11519218
Smeriglio, E., Imbesi, M., Xiao, J., Smeriglio, A., & Trombetta, D. (2026) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2026.1924338
Toward Elderly-Care-Oriented Smart Home HAR via Sensor-Aligned Activity Taxonomy
Tran, L. B. N., Pham, D. D., Le, H. S., Huynh, K. T., Nguyen, S., Le, T. D., & Tuyen, N. T. V. (2026) · International Conference on Applying New Technology in Green Buildings (ATiGB) · doi:10.1109/atigb70203.2026.11628469
Narasinghe, N. M., Obeng, C. P., Neththikumara, S., Vazquez, E. A., & Striker, R. (2026) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit68936.2026.11670383
Towards Human‐Centered Intelligent Inhabitable Spaces: A Review
Kumar, N., Lv, G., Green, K. E., & Walker, I. D. (2026) · Advanced Intelligent Systems · doi:10.1002/aisy.70538
AI‐Driven Early Disease Detection
Deepa, K., Banu, N. S., & Dhanraj, G. (2026) · Wiley eBooks · doi:10.1002/9781394477623.ch4
Context-Awareness in Task Automation Services by Distributed Event Processing
Coronado, M., Bruns, R., Dunkel, J., & Stipković, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-20370-6_15
Metadata
- Authors
- Rashidi, P., & Cook, D. J.
- Affiliations
- Washington State University (US)
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
- Published
- 2009-09
- Volume
- 39
- Issue
- 5
- Pages
- 949-959
- ISSN
- 1083-4427 (ISSN-L)
- 1558-2426
- OpenAlex
- W2121269968
- Cited by
- 443
- References
- 25
- Citations by year
- 2026: 10 · 2025: 16 · 2024: 13 · 2023: 16 · 2022: 19 · 2021: 24 · 2020: 31 · 2019: 41 · 2018: 30 · 2017: 37 · 2016: 21 · 2015: 35 · 2014: 36 · 2013: 34 · 2012: 30
- FWCI
- 29.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Keywords
- home automation, CASAS, user modeling, implicit feedback, model updating
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