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
Cited by
Sorted by citations
Items 201–300 of 434
A Continuous-Time Model-Based Approach to Activity Recognition for Ambient Assisted Living
Carnevali, L., Nugent, C., Patara, F., & Vicario, E. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22264-6_3 · Cited by 9
Privacy preserving infrared sensor array based indoor location awareness
Kobiyama, Y., Zhao, Q., & Omomo, K. (2016) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2016.7844426 · Cited by 9
CoPI: Enabling Probabilistic Conflict Prediction in Smart Space Through Context-awareness
Hua, J., Yu, H., Lee, S., Adal, H. M., Milhaupt, C., Roman, G.-C., & Julien, C. (2022) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1109/iotdi54339.2022.00012 · Cited by 9
Kakavand, Z., Shirehjini, A. A. N., Moghaddam, M. G., & Shirmohammadi, S. (2023) · International Journal of Child-Computer Interaction · doi:10.1016/j.ijcci.2023.100594 · Cited by 9
User’s behavior study for smart houses occupant prediction
Kadouche, R., Chikhaoui, B., & Abdulrazak, B. (2010) · Annals of Telecommunications · doi:10.1007/s12243-010-0166-2 · Cited by 9
Levels of Abstraction for Behavior Modeling in the GerHome Project
Pomponio, L., Goc, M. L., Anfosso, A., & Pascual, É. (2012) · International Journal of E-Health and Medical Communications · doi:10.4018/jehmc.2012070102 · Cited by 9
Fusion system based on multi-agent systems to merge data from WSN
Rodrı́guez, S., Zato, C., Corchado, J. M., & Li, T. (2014) · Cited by 9
Activities suggestion based on emotions in AAL environments
Costa, Â., Rincon, J. A., Carrascosa, C., Nováis, P., & Julián, V. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.01.002 · Cited by 9
An ambient intelligence approach for learning in smart robotic environments
Bacciu, D., Rocco, M. D., Dragone, M., Gallicchio, C., Micheli, A., & Saffiotti, A. (2019) · Computational Intelligence · doi:10.1111/coin.12233 · Cited by 9
End-to-End Multi-Modal Behavioral Context Recognition in a Real-Life Setting
Saeed, A., Özçelebi, T., Trajanovski, S., & Lukkien, J. J. (2019) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion43075.2019.9011194 · Cited by 9
Handosa, M., Dasgupta, A., Manuel, M., & Gračanin, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50344-4_4 · Cited by 9
Hierarchical Multi-Classification for Sensor-based Badminton Activity Recognition
Wang, Y., Ma, J., Li, X., & Zhong, A. (2020) · International Conference on Signal Processing · doi:10.1109/icsp48669.2020.9320935 · Cited by 9
Adapted Long Short-Term Memory (LSTM) for Concurrent\\ Human Activity Recognition
Thapa, K., Al, Z. M. A., & Yang, S. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.015660 · Cited by 9
An Improved Position Prediction Algorithm Based on Active LeZi in Smart Home
Fang, H., & Ruan, J. (2012) · International Conference on Computer Science and Service System · doi:10.1109/csss.2012.433 · Cited by 9
A C-LSTM Neural Network for Human Activity Recognition Using Wearables
Yu, S., Qin, L., & Yin, Q. (2018) · International Symposium on Sensing and Instrumentation in 5G and IoT Era (ISSI) · doi:10.1109/issi.2018.8538129 · Cited by 9
An ontology-based framework for a Less Invasive Domestic Management System (LIDoMS)
Spoladore, D., Mondellini, M., Sacco, M., Spoladore, D., & Trombetta, A. (2020) · International Conference on Intelligent Environments (IE) · doi:10.1109/ie49459.2020.9155023 · Cited by 9
Bock, T. A., Linner, T., Güttler, J., & Iturralde, K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139872034 · Cited by 9
Across-Sensor Feature Learning for Energy-Efficient Activity Recognition on Mobile Devices
Gavrilin, Y., & Khan, A. (2019) · 2019 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851977 · Cited by 8
Composite Activity Recognition in Smart Homes Using Markov Logic Network
Gayathri, K., Elias, S., & Shivashankar, S. (2014) · IEEE International Conference on Ubiquitous Intelligence and Computing, Autonomic and Trusted Computing, and Scalable Computing and Communications (UIC-ATC-ScalCom) · doi:10.1109/uic-atc-scalcom.2014.98 · Cited by 8
Smart TV-Based Lifelogging Systems: Current Trends, Challenges, and the Road Ahead
Khan, M., Khusro, S., & Alam, I. (2021) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-75123-4_2 · Cited by 8
Yao, M., Cheng, D., Zhang, L., Liu, L., Song, S. J., Wu, H., & Song, A. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112444 · Cited by 8
Analyzing correlation of resident activities based on infrared sensors
Kobiyama, Y., Zhao, Q., Omomo, K., & Taya, M. (2015) · International Conference on Awareness Science and Technology (iCAST) · doi:10.1109/icawst.2015.7314011 · Cited by 8
System Design for Coordinated Multi-robot Assistance Deployment in Smart Spaces
Papadakis, P., Lohr, C., Lujak, M., Karami, A., Kanellos, I., Lozenguez, G., & Fleury, A. (2018) · IEEE International Conference on Robotic Computing (IRC) · doi:10.1109/irc.2018.00068 · Cited by 8
Ambient Assisted Living Tools for a Sustanaible Aging Society
Bleda, A. L., Maestre, R., Jara, A. J., & Skármeta, A. (2014) · Modeling and optimization in science and technologies · doi:10.1007/978-3-319-06704-9_9 · Cited by 8
Real-time monitoring of high-level states in smart environments
Corno, F., & Razzak, F. (2015) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-150310 · Cited by 8
Logic-Based Incremental Process Mining in Smart Environments
Ferilli, S., Carolis, B. N. D., & Redavid, D. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38577-3_40 · Cited by 8
An Introduction to Cloud-Based Pervasive Healthcare Systems
Poorejbari, S., & Vahdat‐Nejad, H. (2014) · doi:10.5555/2762722.2762754 · Cited by 8
Recognition of Frequently Appeared Locations/Activities Based on Infrared Sensor Array
Kobiyama, Y., Zhao, Q., Ota, R., & Shoichi, I. (2017) · IEEE International Conference on Cybernetics (CYBCONF) · doi:10.1109/cybconf.2017.7985801 · Cited by 8
Personalized adaptation in pervasive systems via non-functional requirements
Serral, E., Sernani, P., & Dalpiaz, F. (2017) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-017-0611-4 · Cited by 8
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learningPreprint
Vono, M., Plassier, V., Durmus, A., Dieuleveut, A., & Moulines, É. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.00797 · Cited by 9
A Deep Learning Architecture for Human Activity Recognition Using PPG and Inertial Sensor Dataset
Bondugula, R. K., Sivangi, K. B., & Udgata, S. K. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-1412-6_48 · Cited by 7
Sepahvand, M., Meqdad, M. N., & Abdali-Mohammadi, F. (2024) · International Journal of Computers and Applications · doi:10.1080/1206212x.2024.2426501 · Cited by 7
Synergy Effect in Platform Ecosystem: A Quantitative Analysis of Smart Home Products
Tang, R., Inoue, Y., & Tsujimoto, M. (2025) · IEEE Access · doi:10.1109/access.2025.3531993 · Cited by 7
Gouda, S. K., Choudhry, A., Satpathy, S. P., Shukla, K. K., Dash, A. K., & Pasayat, A. K. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128730 · Cited by 7
HOBOT: A customizable home management system with a surveillance RoBOT
El‐Shafei, M., Shalati, A. A., Rehayel, M., & Damaj, I. (2014) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece.2014.6901088 · Cited by 7
Learning Algorithm for Human Activity Detection in Smart Environments
Botón-Fernández, V., & Lozano-Tello, A. (2011) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2011.80 · Cited by 7
Analyzing resident life cycle based on short-time cross-correlation
Kobiyama, Y., Zhao, Q., Omomo, K., & Taya, M. (2015) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2015.7372877 · Cited by 7
Effect of Reduced Dimensionality on Deep learning for Human Activity Recognition
Kumari, G., Chakraborty, J., & Nandy, A. (2020) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt49239.2020.9225419 · Cited by 7
Improvement of Building Automation System
Levin, M. S., Andrushevich, A., & Klapproth, A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21827-9_47 · Cited by 7
Domain Selection and Adaptation in Smart Homes
Rashidi, P., & Cook, D. J. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21535-3_3 · Cited by 7
Self-citationStream Sequence Mining for Human Activity Discovery
Rashidi, P. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-398532-3.00005-1 · Cited by 7
Self-citationDeep learning object-recognition in a design-to-robotic-production and -operation implementation
Cheng, A. L., Bier, H., & Mostafavi, S. (2017) · IEEE Ecuador Technical Chapters Meeting (ETCM) · doi:10.1109/etcm.2017.8247495 · Cited by 7
Probabilistic Ontology Reasoning in Ambient Assistance: Predicting Human Actions
Lunardi, G. M., Machado, G. M., Machot, F. A., Maran, V., Machado, A., Mayr, H. C., Shekhovtsov, V. A., & de Oliveira, J. P. M. (2018) · International Conference on Advanced Information Networking and Applications (AINA) · doi:10.1109/aina.2018.00092 · Cited by 7
EUD4SH: A EUD Model for the Smart Home
Caivano, D., Cassano, F., Fogli, D., & Piccinno, A. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-01746-0_10 · Cited by 7
A Self-adaptive System for Improving Autonomy and Public Spaces Accessibility for Elderly
Triki, S., & Hanachi, C. (2017) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-59394-4_6 · Cited by 7
Cognitive Smart Cities and Deep Learning: A Classification Framework
Lima, S., & Terán, L. (2019) · International Conference on eDemocracy & eGovernment · doi:10.1109/icedeg.2019.8734346 · Cited by 7
Learning behavioral context recognition with multi-stream temporal convolutional networksPreprint
Saeed, A., Özçelebi, T., Trajanovski, S., & Lukkien, J. J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.08766 · Cited by 7
Concept drift challenge in multimedia anomaly detection: A case study with facial datasets
Kumari, P., Choudhary, P., Kujur, V., Atrey, P. K., & Saini, M. K. (2024) · Signal Processing Image Communication · doi:10.1016/j.image.2024.117100 · Cited by 6
IoT data streams: concepts and models
SCHNEIDER, P. L., & Xhafa, F. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823818-9.00011-0 · Cited by 6
A Framework Towards Ambient Assisted Living Enhanced by Service Robots
Toupas, P., Tsamis, G., Kargakos, A., Giakoumis, D., Votis, K., & Tzovaras, D. K. (2023) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3594806.3596532 · Cited by 6
User oriented smart connected product and smart environment: a systematic literature review
Xia, L., Li, K., Zhou, T., Zhang, L., & Fu, W. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-12725-y · Cited by 6
AdAPT -- A Dynamic Approach for Activity Prediction and Tracking for Ambient Intelligence
Frey, J. (2013) · International Conference on Intelligent Environments (IE) · doi:10.1109/ie.2013.38 · Cited by 6
Behavior Analysis for Aging-in-Place using Similarity Heatmaps
Eldib, M., Bo, N. B., Deboeverie, F., Xie, X., Philips, W. R., & Aghajan, H. K. (2014) · International Conference on Distributed Smart Cameras · doi:10.1145/2659021.2659038 · Cited by 6
Describing and classifying spatial and temporal contexts with OWL DL in Ubiquitous Robotics
Scalmato, A., Sgorbissa, A., & Zaccaria, R. (2012) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2012.6224835 · Cited by 6
Behavioural Biometrics Using Electricity Load Profiles
Bicego, M., Recchia, F., Farinelli, A., Ramchurn, S. D., & Grosso, E. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.310 · Cited by 6
Towards a User Feedback Approach for Smart Homes: An Explorative Interview Study
Stade, M. J. C., Seyff, N., Baikenova, A., & Scherr, S. A. (2020) · doi:10.1109/crowdre51214.2020.00008 · Cited by 6
ADLs Recognition of an Elderly Person and Wellness Determination
Suryadevara, N. K., & Mukhopadhyay, S. C. (2015) · Smart sensors, measurement and instrumentation · doi:10.1007/978-3-319-13557-1_4 · Cited by 6
Increasing Prediction Accuracy for Human Activity Recognition Using Optimized Hyperparameters
Darji, N. R., & Ajila, S. A. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378000 · Cited by 6
Designing for user attention: A method for supporting unobtrusive routine tasks
Gil, M., Serral, E., Valderas, P., & Pelechano, V. (2013) · Science of Computer Programming · doi:10.1016/j.scico.2013.03.002 · Cited by 6
Moghaddam, E. S., Ellis, C., & Hejrati, B. (2025) · IEEE Access · doi:10.1109/access.2025.3606339 · Cited by 6
Elaborate Synthetic Data Generation for Internet of Things Services at Smart Home Environment
Myung, R., Choi, S., Choi, W., Yu, H., Lee, D., & Lee, E. (2016) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci.2016.0050 · Cited by 5
Voice Recognition Technology : A Review
Fegade, S. V., Chaturvedi, A., & Agarwal, M. (2021) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-1807 · Cited by 5
Architecting Smart Home Environments for Healthcare : A Database-Centric Approach
de Morais, W. O. (2015) · Cited by 5
Dynamic Partial Reconfiguration in Embedded Systems for Intelligent Environments
Echanobe, J., del Campo, I., Finker, R., & Basterretxea, K. (2012) · International Conference on Intelligent Environments (IE) · doi:10.1109/ie.2012.35 · Cited by 5
A cognitive model for recognizing human behaviours in smart homes
Mastrogiovanni, F., Sgorbissa, A., & Zaccaria, R. (2010) · Annals of Telecommunications · doi:10.1007/s12243-010-0171-5 · Cited by 5
Designing Smart Home Interfaces
Ceccacci, S., & Mengoni, M. (2017) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3056540.3056556 · Cited by 5
Recognising Activities at Home
Jiang, J., Pozza, R., Gunnarsdóttir, K., Gilbert, N., & Moessner, K. (2017) · International Conference on Future Networks and Distributed Systems (ICFNDS) · doi:10.1145/3102304.3102321 · Cited by 5
Analytical Framework for Research on Smart Homes and Their Users
Hargreaves, T., & Wilson, C. (2017) · Human-computer interaction series · doi:10.1007/978-3-319-68018-7_2 · Cited by 5
The Cornerstones of Smart Home Research for Healthcare
Bouchard, K., Hao, J., Bouchard, B., Gaboury, S., Moutacalli, M. T., Gouin-Vallerand, C., Ngankam, H. K., Pigot, H., & Giroux, S. (2018) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-77911-9_11 · Cited by 5
Design and Implementation of a Self-Learner Smart Home System Using Machine Learning Algorithms
Güven, C. T., & Acı, M. (2022) · Information Technology And Control · doi:10.5755/j01.itc.51.3.31273 · Cited by 5
Bridging the Gap between Smart Home and Agents
Britz, J., Frey, J., & Alexandersson, J. (2014) · International Conference on Intelligent Environments (IE) · doi:10.1109/ie.2014.12 · Cited by 5
Learning in Dynamic Environments
Sayed‐Mouchaweh, M. (2015) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-3-319-25667-2_2 · Cited by 5
Secured Speech Industrial Automation Based on Raspberry Pi and IoT
Rao, B., & Chandrakanth, H. (2018) · International journal of intelligent engineering and systems · doi:10.22266/ijies2018.1231.23 · Cited by 5
Influence of time and length size feature selections for human activity sequences recognition
Fang, H., Chen, L., & Srinivasan, R. (2013) · ISA Transactions · doi:10.1016/j.isatra.2013.09.001 · Cited by 5
Smart Environment Monitoring Models Using Cloud‐Based Data Analytics: A Comprehensive Study
Borkar, P. S., & Thakur, R. (2021) · Wiley eBooks · doi:10.1002/9781119785873.ch10 · Cited by 5
Next-Generation Clinical Health Leveraging Intelligent Systems and IoT for Better Care
Raja, R. V., Joel, M. R., Kumar, V. V., NagaLakshmi, B., Krishnan, S., & Ishwarya, K. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-8990-4.ch009 · Cited by 4
Indoor Localization and ADL Monitoring via RSSI-Driven ML with Feedback Process
Αντωνόπουλος, Κ., Skandamis, T., Alogdianakis, G., Faliagka, E., Antonopoulos, C., & Voros, N. S. (2025) · Electronics · doi:10.3390/electronics14193759 · Cited by 4
An event-based approach to multi-modal activity modeling and recognition
Pijl, M., van de Par, S., & Shan, C. (2010) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom.2010.5466986 · Cited by 4
Edgcomb, A., & Vahid, F. (2012) · International Health Informatics Symposium · doi:10.1145/2110363.2110387 · Cited by 4
Assisted living technologies for older adults
Rashidi, P. (2012) · International Health Informatics Symposium · doi:10.1145/2110363.2110478 · Cited by 4
Self-citationSustainable architecture and human comfort through adaptive distributed systems
Nagy, Z., Rossi, D., & Schlueter, A. (2012) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2012.6197520 · Cited by 4
A Method to Design a Smart Home Interface
Ceccacci, S., Germani, M., & Mengoni, M. (2013) · Lecture notes in production engineering · doi:10.1007/978-3-642-30817-8_90 · Cited by 4
Qualitative Tracking of Objects in a Smart Home
Bergeron, F., Bouchard, K., Giroux, S., Gaboury, S., & Bouchard, B. (2016) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/2910674.2910700 · Cited by 4
Living preference modeling of smart homes for different target groups
Allameh, E., Jozam, M. H., & de Vries, B. (2018) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-180484 · Cited by 4
Autonomic goal-oriented device management for Smart Environments
Sanaullah, M., Corno, F., & Razzak, F. (2015) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-150329 · Cited by 4
Localising agents in multiple-occupant intelligent environments
Akhlaghinia, M. J., Lotfi, A., & Langensiepen, C. S. (2010) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzzy.2010.5584379 · Cited by 4
IoT Based Low Cost and Ageing Healthcare Monitoring System
Sree, K. N., & Rao, G. M. (2020) · International Journal of Engineering and Advanced Technology · doi:10.35940/ijeat.f1192.089620 · Cited by 4
Ambient intelligence system for controlling home automation installations
Valiente-Rocha, P. A., Redondo-García, J. L., & Lozano-Tello, A. (2010) · Iberian Conference on Information Systems and Technologies (CISTI) · Cited by 4
Levin, M. S. (2013) · Journal of Communications Technology and Electronics · doi:10.1134/s1064226913060168 · Cited by 4
Specifying software features for composition: A tool-supported approach
Tun, T. T., Laney, R., Yu, Y., & Nuseibeh, B. (2013) · Computer Networks · doi:10.1016/j.comnet.2013.02.022 · Cited by 4
Hassan, M., Umar, M., Bermak, A., Ali, A. A. S., & Amira, A. (2016) · IEEE International Symposium on Systems Engineering · doi:10.1109/syseng.2016.7753122 · Cited by 4
Composition of Management System for Smart HomesPreprint
Levin, M. S., Andrushevich, A., & Klapproth, A. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.3844 · Cited by 4
Nasir, I., Khan, M. H., & Farid, M. S. (2025) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2025.108218 · Cited by 3
Learning action sequences for decision-making in home automation systems
Botón-Fernández, V., Redondo-García, J. L., & Lozano-Tello, A. (2012) · Iberian Conference on Information Systems and Technologies (CISTI) · Cited by 3
Interdisciplinary perspectives on privacy awareness in lifelogging technology development
Wilkowska, W., Offermann, J., Colonna, L., Flórez‐Revuelta, F., Climent-Pérez, P., Mihailidis, A., Poli, A., Spinsante, S., & Ziefle, M. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-022-04486-5 · Cited by 6
A User Personalized and Adaptative Smart Home using Large Language Models
Rey-Jouanchicot, J., Bottaro, A., Campo, É., Bouraoui, J.-L., Vigouroux, N., & Vella, F. (2025) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc54725.2025.10975953 · Cited by 3
A Federated Learning-Based Framework for Accurately Identifying Human Activity in the Environment
Al-Blihed, N. S., & Ibrahim, D. M. (2025) · IEEE Access · doi:10.1109/access.2025.3583556 · Cited by 5
Lozano-Tello, A., & Botón-Fernández, V. (2012) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-28765-7_61 · Cited by 3
User profiling and micro-accounting for smart energy management
Caruso, M., Mecella, M., Baldoni, R., Querzoni, L., & Cerocchi, A. (2013) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/2517351.2517414 · Cited by 3
Towards Robots-Assisted Ambient Intelligence
Lujak, M., Bouraqadi, N., Doniec, A., Fabresse, L., Fleury, A., Karami, A., & Lozenguez, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01713-2_34 · Cited by 3
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
