{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T10:18:04Z","timestamp":1784024284088,"version":"3.55.0"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,13]],"date-time":"2019-02-13T00:00:00Z","timestamp":1550016000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Background: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle monitoring in an ambient assisted living (AAL) system by diagnosing conduct and distinguishing variation from the norm with the slightest conceivable fake alert. In pursuing this aim, the main objective is to fill the knowledge gap of two contextual observations (i.e., day and time) in the frequent behavior modeling for an individual in AAL. Each sensing category has its advantages and restrictions. Only a single type of sensing unit may not manage composite states in practice and lose the activity of daily living. To boost the efficiency of the system, we offer an exceptional sensor data fusion technique through different sensing modalities; Methods: As behaviors may also change according to other contextual observations, including seasonal, weather (or temperature), and social interaction, we propose the design of a novel activity learning model by adding behavioral observations, which we name as the Wellness indices analysis model; Results: The ground-truth data are collected from four elderly houses, including daily activities, with a sample size of three hundred days plus sensor activation. The investigation results validate the success of our method. The new feature set from sensor data fusion enhances the system accuracy to (98.17% \u00b1 0.95) from (80.81% \u00b1 0.68). The performance evaluation parameters of the proposed model for ADL recognition are recorded for the 14 selected activities. These parameters are Sensitivity (0.9852), Specificity (0.9988), Accuracy (0.9974), F1 score (0.9851), False Negative Rate (0.0130).<\/jats:p>","DOI":"10.3390\/s19040766","type":"journal-article","created":{"date-parts":[[2019,2,14]],"date-time":"2019-02-14T03:21:46Z","timestamp":1550114506000},"page":"766","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection"],"prefix":"10.3390","volume":"19","author":[{"given":"Hemant","family":"Ghayvat","sequence":"first","affiliation":[{"name":"CIME, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Awais","sequence":"additional","affiliation":[{"name":"CIME, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4507-1844","authenticated-orcid":false,"given":"Sharnil","family":"Pandya","sequence":"additional","affiliation":[{"name":"Computer Science &amp; Engineering Department, Navrachana University, Vadodara, Gujarat 391410, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Ren","sequence":"additional","affiliation":[{"name":"CIME, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeed","family":"Akbarzadeh","sequence":"additional","affiliation":[{"name":"CIME, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Subhas","family":"Chandra Mukhopadhyay","sequence":"additional","affiliation":[{"name":"Mechanical\/Electronics Engineering, Macquarie University, Sydney NSW 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"CIME, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2786-0273","authenticated-orcid":false,"given":"Prosanta","family":"Gope","sequence":"additional","affiliation":[{"name":"School of Engineering and Computer Science, University of Hull, Hull HU1 1DB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arpita","family":"Chouhan","sequence":"additional","affiliation":[{"name":"CIME, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"CIME, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Uddin, M., Khaksar, W., and Torresen, J. (2018). Ambient sensors for elderly care and independent living: A survey. Sensors, 18.","DOI":"10.3390\/s18072027"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ghayvat, H., Mukhopadhyay, S., Shenjie, B., Chouhan, A., and Chen, W. (2018, January 14\u201317). Smart home based ambient assisted living: Recognition of anomaly in the activity of daily living for an elderly living alone. Proceedings of the 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Houston, TX, USA.","DOI":"10.1109\/I2MTC.2018.8409885"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.arr.2014.05.002","article-title":"The impact of exercise on the cognitive functioning of healthy older adults: A systematic review and meta-analysis","volume":"16","author":"Kelly","year":"2014","journal-title":"Aging Res. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s12652-016-0374-3","article-title":"Exploring the ambient assisted living domain: A systematic review","volume":"8","author":"Calvaresi","year":"2017","journal-title":"J. Amb. Intel. Hum. Comp."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"10779","DOI":"10.1007\/s11042-016-3267-8","article-title":"Towards online and personalized daily activity recognition, habit modeling, and anomaly detection for the solitary elderly through unobtrusive sensing","volume":"76","author":"Meng","year":"2017","journal-title":"Multimedia Tools Appl."},{"key":"ref_6","unstructured":"Pulkkinen, T., Sallinen, M., Son, J., Park, J.H., and Lee, Y.H. (2012, January 9\u201312). Home network semantic modeling and reasoning\u2014A case study. Proceedings of the 15th International Conference on information fusion, Singapore."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/j.pmcj.2011.06.004","article-title":"Centinela: A human activity recognition system based on acceleration and vital sign data","volume":"8","author":"Lara","year":"2012","journal-title":"Pervasive Mob. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/TMECH.2014.2301716","article-title":"WSN-based smart sensors and actuator for power management in intelligent buildings","volume":"20","author":"Suryadevara","year":"2015","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5363","DOI":"10.3390\/s120505363","article-title":"A framework for supervising lifestyle diseases using long-term activity monitoring","volume":"12","author":"Han","year":"2012","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1007\/s11042-011-0919-6","article-title":"Ontology-based healthcare context information model to implement ubiquitous environment","volume":"71","author":"Kim","year":"2014","journal-title":"Multimed. Tools Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"239","DOI":"10.14257\/ijsh.2013.7.5.24","article-title":"Ontology model-based situation and socially-aware health care service in a smart home environment","volume":"7","author":"Lee","year":"2013","journal-title":"Int. J. Smart Home"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"McAvoy, L.M., Chen, L., and Donnelly, M. (2013). A generic upper-level ontological model for context-aware applications within smart environments. International Conference on Ubiquitous Computing and Ambient Intelligence, Carrillo, Costa Rica, 2\u20136 December 2013, Springer International Publishing.","DOI":"10.1007\/978-3-319-03176-7_35"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s12652-011-0063-1","article-title":"An ontological framework for activity monitoring and reminder reasoning in an assisted environment","volume":"4","author":"Zhang","year":"2013","journal-title":"J. Amb. Intel. Hum. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.future.2014.02.014","article-title":"Combining ontological and temporal formalisms for composite activity modelling and recognition in smart homes","volume":"39","author":"Okeyo","year":"2014","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1109\/JBHI.2016.2593980","article-title":"Delta Features from Ambient Sensor Data are Good Predictors of Change in Functional Health","volume":"21","author":"Robben","year":"2017","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/JBHI.2016.2636665","article-title":"Deep learning for health informatics","volume":"21","author":"Wong","year":"2017","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/0020-7101(95)01139-6","article-title":"Remote monitoring of health status of the elderly at home. A multidisciplinary project on aging at the University of New South Wales","volume":"40","author":"Celler","year":"1995","journal-title":"Int. J. Bio-Med. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7341","DOI":"10.1109\/JSEN.2015.2475626","article-title":"Wellness Sensors Networks: A Proposal and Implementation for Smart Home to Assisted Living","volume":"15","author":"Ghayvat","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_19","first-page":"33","article-title":"Internet of Things for smart homes and buildings: Opportunities and Challenges","volume":"3","author":"Ghayvat","year":"2015","journal-title":"Aust. J. Telecommun. Digit. Econ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1080\/17517575.2013.776118","article-title":"Design of a terminal solution for integration of in-home health care devices and services toward the Internet-of-Things","volume":"9","author":"Pang","year":"2015","journal-title":"Enterp. Inf. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s007790170019","article-title":"Understanding and using context","volume":"5","author":"Dey","year":"2001","journal-title":"Pers. Ubiquit. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zimmermann, A., Lorenz, A., and Oppermann, R. (2007). An operational definition of context. International and Interdisciplinary Conference on Modeling and Using Context, Roskilde, Denmark, 20\u201324 August 2017, Springer.","DOI":"10.1007\/978-3-540-74255-5_42"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1207\/S15327051HCI16234_11","article-title":"An infrastructure approach to context-aware computing","volume":"16","author":"Hong","year":"2001","journal-title":"Hum. Comput. Interact."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cardinaux, F., Brownsell, S., Hawley, M., and Bradley, D. (2008). Modelling of behavioural patterns for abnormality detection in the context of lifestyle reassurance. Iberoamerican Congress on Pattern Recognition, Progress in Pattern Recognition, Image Analysis and Applications, Havana, Cuba, 9\u201312 September 2008, Springer.","DOI":"10.1007\/978-3-540-85920-8_30"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1109\/TITB.2007.904157","article-title":"Behavioral patterns of older adults in assisted living","volume":"12","author":"Virone","year":"2008","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_26","first-page":"60","article-title":"Home Telehealth: Connecting Care Within the Community","volume":"76","author":"Patterson","year":"2007","journal-title":"Ulster Med. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1109\/TBME.2002.805452","article-title":"A system for automatic measurement of circadian activity deviations in telemedicine","volume":"49","author":"Virone","year":"2002","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Monekosso, D.N., and Remagnino, P. (2009). Anomalous behavior detection: Supporting independent living. Intell. Environ., 33\u201348.","DOI":"10.1007\/978-1-84800-346-0_3"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mori, T., Fujii, A., Shimosaka, M., Noguchi, H., and Sato, T. (2007, January 6\u20138). Typical behavior patterns extraction and anomaly detection algorithm based on accumulated home sensor data. Proceedings of the Future Generation Communication and Networking (FGCN 2007), Jeju, South Korea.","DOI":"10.1109\/FGCN.2007.226"},{"key":"ref_30","unstructured":"Kang, W., Shin, D., and Shin, D. (2010, January 29\u201331). Detecting and predicting of abnormal behavior using hierarchical markov model in smart home network. Proceedings of the 2010 IEEE 17th International Conference on Industrial Engineering and Engineering Management, Xiamen, China."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1016\/j.patcog.2007.10.022","article-title":"A daily behavior enabled hidden Markov model for human behavior understanding","volume":"41","author":"Chung","year":"2008","journal-title":"Pattern Recogn."},{"key":"ref_32","unstructured":"Duong, T.V., Bui, H.H., Phung, D.Q., and Venkatesh, S. (2005, January 20\u201325). Activity recognition and abnormality detection with the switching hidden semi-markov model. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1016\/j.patcog.2014.07.007","article-title":"A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living","volume":"48","author":"Forkan","year":"2015","journal-title":"Pattern Recogn."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Spagnolo, P., Mazzeo, P., and Distante, C. (2014). Data Fusion with a Dense Sensor Network for Anomaly Detection in Smart Homes. Human Behavior Understanding in Networked Sensing, Springer.","DOI":"10.1007\/978-3-319-10807-0"},{"key":"ref_35","first-page":"1","article-title":"Anomaly detection in user daily patterns in smart-home environment","volume":"3","author":"Jakab","year":"2013","journal-title":"J. Sel. Areas Health Inform."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Alberg, D. (2019). Big Data Time Series Stream Data Segmentation Methods. Encyclopedia of Information Science and Technology, IGI Global. [4th ed.].","DOI":"10.4018\/978-1-5225-7598-6.ch004"},{"key":"ref_37","unstructured":"Li, M., O\u2019Grady, M., Gu, X., Alawlaqi, M.A., and O\u2019Hare, G. (2018). Time-bounded Activity Recognition for Ambient Assisted Living. IEEE Trans. Emerg. Top. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5262","DOI":"10.1109\/ACCESS.2017.2684913","article-title":"Improving activity recognition accuracy in ambient-assisted living systems by automated feature engineering","volume":"5","author":"Zdravevski","year":"2017","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"13131","DOI":"10.1109\/ACCESS.2017.2719921","article-title":"Mining human activity patterns from smart home big data for health care applications","volume":"5","author":"Yassine","year":"2017","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.future.2018.09.004","article-title":"Learning and managing context enriched behavior patterns in smart homes","volume":"91","author":"Lago","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sfar, H., Bouzeghoub, A., Ramoly, N., and Boudy, J. (2017). AGACY monitoring: A hybrid model for activity recognition and uncertainty handling. European Semantic Web Conference, Portoro\u017e, Slovenia, 28 May\u20131 June 2017, Springer.","DOI":"10.1007\/978-3-319-58068-5_16"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lago, P., Jim\u00e9nez-Guar\u00edn, C., and Roncancio, C. (2015). Contextualized behavior patterns for ambient assisted living. Human Behavior Understanding, Osaka, Japan, 8 September 2015, Springer.","DOI":"10.1007\/978-3-319-24195-1_10"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/MSP.2015.2503881","article-title":"Monitoring activities of daily living in smart homes: Understanding human behavior","volume":"33","author":"Debes","year":"2016","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.jbi.2018.03.009","article-title":"Automatic assessment of functional health decline in older adults based on smart home data","volume":"81","author":"Aramendi","year":"2018","journal-title":"J. Biomed. Inform."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/4\/766\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:31:48Z","timestamp":1760185908000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/4\/766"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,13]]},"references-count":44,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["s19040766"],"URL":"https:\/\/doi.org\/10.3390\/s19040766","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,13]]}}}