{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T17:05:11Z","timestamp":1790787911170,"version":"4.1.0"},"reference-count":61,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T00:00:00Z","timestamp":1745625600000},"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>(1) Background and objective: Mobility is crucial for healthy aging, and its loss significantly impacts the quality of life, healthcare costs, and mortality among older adults. Clinical mobility assessment methods, though precise, are resource-intensive and economically impractical, and most of the existing solutions for automatic detection of mobility anomalies are either obtrusive or improper for long time monitoring. This study explores the feasibility of using non-intrusive, low-cost binary sensors for continuous, remote detection of mobility anomalies in older adults, aiming to identify both sudden mobility events and gradual mobility loss. (2) Method: The study utilized publicly available datasets (CASAS Aruba and HH120) containing annotated activity data recorded from binary sensors installed in residential environments. After data preprocessing\u2014including filtering irrelevant sensor events and aggregation into behaviorally meaningful places (BMPs)\u2014a time series forecasting model (Prophet) was used to predict normal mobility patterns. A fuzzy inference module analyzed deviations between observed and predicted sensor data to determine the probability of mobility anomalies. (3) Results: The system effectively identified periods of prolonged inactivity indicative of potential falls or other mobility disruptions. Preliminary evaluation indicated a detection rate of approximately 77\u201381% for point mobility anomalies, with a false positive rate ranging from 12 to 16%. Additionally, the approach successfully detected simulated gradual declines in mobility (1% per day reduction), evidenced by statistically significant regression trends in activity levels over time. (4) Conclusions: The study argues that non-intrusive binary sensors, combined with lightweight forecasting models and fuzzy inference, may provide a practical and scalable solution for detecting mobility anomalies in older adults. Although performance can be further enhanced through improved data preprocessing, predictive modeling, and anomaly threshold tuning, the proposed system effectively addresses key limitations of existing mobility assessment approaches.<\/jats:p>","DOI":"10.3390\/s25092755","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T06:23:32Z","timestamp":1745821412000},"page":"2755","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Non-Intrusive Monitoring and Detection of Mobility Loss in Older Adults Using Binary Sensors"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4795-4699","authenticated-orcid":false,"given":"Ioan","family":"Susnea","sequence":"first","affiliation":[{"name":"Department of Computers and Information Technology, University Dunarea de Jos of Galati, 800008 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emilia","family":"Pecheanu","sequence":"additional","affiliation":[{"name":"Department of Computers and Information Technology, University Dunarea de Jos of Galati, 800008 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0935-4713","authenticated-orcid":false,"given":"Adina","family":"Cocu","sequence":"additional","affiliation":[{"name":"Department of Computers and Information Technology, University Dunarea de Jos of Galati, 800008 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1317-6676","authenticated-orcid":false,"given":"Adrian","family":"Istrate","sequence":"additional","affiliation":[{"name":"Department of Computers and Information Technology, University Dunarea de Jos of Galati, 800008 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1849-3072","authenticated-orcid":false,"given":"Catalin","family":"Anghel","sequence":"additional","affiliation":[{"name":"Department of Computers and Information Technology, University Dunarea de Jos of Galati, 800008 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7539-4159","authenticated-orcid":false,"given":"Paul","family":"Iacobescu","sequence":"additional","affiliation":[{"name":"Department of Computers and Information Technology, University Dunarea de Jos of Galati, 800008 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,26]]},"reference":[{"key":"ref_1","first-page":"190","article-title":"Characterizing Mobility Limitations Among Older African American Men","volume":"110","author":"Mitchell","year":"2018","journal-title":"J. Natl. Med. Assoc."},{"key":"ref_2","first-page":"397","article-title":"An Aging and Values-Driven Theory of Mobility","volume":"47","author":"Chua","year":"2023","journal-title":"Act. Adapt. Aging"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1007\/s11606-010-1543-2","article-title":"Ability to Walk 1\/4 Mile Predicts Subsequent Disability, Mortality, and Health Care Costs","volume":"26","author":"Hardy","year":"2011","journal-title":"J. Gen. Intern. Med."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Freiberger, E., Sieber, C.C., and Kob, R. (2020). Mobility in Older Community-Dwelling Persons: A Narrative Review. Front. Physiol., 11.","DOI":"10.3389\/fphys.2020.00881"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.gaitpost.2018.06.116","article-title":"Cognition and Mobility Show a Global Association in Middle- and Late-Adulthood: Analyses from the Canadian Longitudinal Study on Aging","volume":"64","author":"Demnitz","year":"2018","journal-title":"Gait Posture"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1123\/japa.2013-0181","article-title":"Instruments to Assess Mobility Limitation in Community-Dwelling Older Adults: A Systematic Review","volume":"23","author":"Chung","year":"2015","journal-title":"J. Aging Phys. Act."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Braun, T., Thiel, C., Peter, R.S., Bahns, C., B\u00fcchele, G., Rapp, K., Becker, C., and Gr\u00fcneberg, C. (2022). Association of Clinical Outcome Assessments of Mobility Capacity and Incident Disability in Community-Dwelling Older Adults\u2014A Systematic Review and Meta-Analysis. medRxiv.","DOI":"10.1101\/2022.03.02.22271795"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Stavropoulos, T.G., Papastergiou, A., Mpaltadoros, L., Nikolopoulos, S., and Kompatsiaris, I. (2020). IoT Wearable Sensors and Devices in Elderly Care: A Literature Review. Sensors, 20.","DOI":"10.3390\/s20102826"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"100121","DOI":"10.1016\/j.sintl.2021.100121","article-title":"Sensors for Daily Life: A Review","volume":"2","author":"Javaid","year":"2021","journal-title":"Sens. Int."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, Z., Kong, J., Qu, M., Zhao, G., and Zhang, C. (2022). Progress in Data Acquisition of Wearable Sensors. Biosensors, 12.","DOI":"10.3390\/bios12100889"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Salcedo, E. (2024). Computer Vision-Based Gait Recognition on the Edge: A Survey on Feature Representations, Models, and Architectures. J. Imaging, 10.","DOI":"10.20944\/preprints202410.0903.v2"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, B., Chen, C., Hu, J., Sayeed, Z., Qi, J., Darwiche, H.F., Little, B.E., Lou, S., Darwish, M., and Foote, C. (2022). Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction. Sensors, 22.","DOI":"10.3390\/s22207960"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cantone, A.A., Esposito, M., Perillo, F.P., Romano, M., Sebillo, M., and Vitiello, G. (2023). Enhancing Elderly Health Monitoring: Achieving Autonomous and Secure Living through the Integration of Artificial Intelligence, Autonomous Robots, and Sensors. Electronics, 12.","DOI":"10.3390\/electronics12183918"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Facchinetti, G., Petrucci, G., Albanesi, B., De Marinis, M.G., and Piredda, M. (2023). Can Smart Home Technologies Help Older Adults Manage Their Chronic Condition? A Systematic Literature Review. Int. J. Environ. Res. Public Health, 20.","DOI":"10.3390\/ijerph20021205"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"113879","DOI":"10.1109\/ACCESS.2021.3094243","article-title":"Internet of Things and Deep Learning Enabled Elderly Fall Detection Model for Smart Homecare","volume":"9","author":"Vaiyapuri","year":"2021","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"114186","DOI":"10.1016\/j.measurement.2024.114186","article-title":"A Comprehensive Review of Elderly Fall Detection Using Wireless Communication and Artificial Intelligence Techniques","volume":"226","author":"Gharghan","year":"2024","journal-title":"Measurement"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"33250","DOI":"10.1109\/ACCESS.2020.2971552","article-title":"Daily Locomotion Recognition and Prediction: A Kinematic Data-Based Machine Learning Approach","volume":"8","author":"Figueiredo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mennella, C., Maniscalco, U., Pietro, G.D., and Esposito, M. (2023). A Deep Learning System to Monitor and Assess Rehabilitation Exercises in Home-Based Remote and Unsupervised Conditions. Comput. Biol. Med., 166.","DOI":"10.1016\/j.compbiomed.2023.107485"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"120288","DOI":"10.1016\/j.eswa.2023.120288","article-title":"Deep Learning-Based Approaches for Human Motion Decoding in Smart Walkers for Rehabilitation","volume":"228","author":"Lopes","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly Detection: A Survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Momin, M.S., Sufian, A., Barman, D., Dutta, P., Dong, M., and Leo, M. (2022). In-Home Older Adults\u2019 Activity Pattern Monitoring Using Depth Sensors: A Review. Sensors, 22.","DOI":"10.3390\/s22239067"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fayad, M., Hachani, M.-Y., Ghoumid, K., Mostefaoui, A., Chouali, S., Picaud, F., Herlem, G., Lajoie, I., and Yahiaoui, R. (2023). Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey. Appl. Sci., 13.","DOI":"10.3390\/app131810352"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Casilari, E., \u00c1lvarez-Marco, M., and Garc\u00eda-Lagos, F. (2020). A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems. Symmetry, 12.","DOI":"10.3390\/sym12040649"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, Z. (2023, January 4\u20136). Detecting Falls Through Convolutional Neural Networks Using Infrared Sensor and Accelerometer. Proceedings of the 2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life Using AI, Robotics and IoT (HONET), Boca Raton, FL, USA.","DOI":"10.1109\/HONET59747.2023.10374742"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"012131","DOI":"10.1088\/1755-1315\/794\/1\/012131","article-title":"Elderly Fall Detection and Warning System","volume":"794","author":"Tanutama","year":"2021","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.neunet.2023.03.042","article-title":"Robust Fall Detection in Video Surveillance Based on Weakly Supervised Learning","volume":"163","author":"Wu","year":"2023","journal-title":"Neural Netw."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.gaitpost.2019.10.006","article-title":"Gait Measurement at Home Using a Single RGB Camera","volume":"76","author":"Yagi","year":"2020","journal-title":"Gait Posture"},{"key":"ref_28","first-page":"1","article-title":"Analysis of Foot-Pressure Data to Classify Mobility Pattern","volume":"7","author":"Chakraborty","year":"2014","journal-title":"Int. J. Smart Sens. Intell. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/978-3-030-03062-9_11","article-title":"Step by Step: Early Detection of Diseases Using an Intelligent Floor","volume":"Volume 11249","author":"Kameas","year":"2018","journal-title":"Ambient Intelligence"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"100541","DOI":"10.1016\/j.iot.2022.100541","article-title":"In-Home Health Monitoring Using Floor-Based Gait Tracking","volume":"19","author":"Hahm","year":"2022","journal-title":"Internet Things"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"94","DOI":"10.4236\/jcc.2018.65008","article-title":"Detecting Dementia-Related Wandering Locomotion of Elders by Leveraging Active Infrared Sensors","volume":"6","author":"Lin","year":"2018","journal-title":"J. Comput. Commun."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/24725579.2018.1530315","article-title":"Multi-Scale Graph Modeling and Analysis of Locomotion Dynamics towards Sensor-Based Dementia Assessment","volume":"9","author":"Cheng","year":"2019","journal-title":"IISE Trans. Healthc. Syst. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Batista, E., Borras, F., Casino, F., and Solanas, A. (2015, January 6\u20138). A Study on the Detection of Wandering Patterns in Human Trajectories. Proceedings of the 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA), Corfu, Greece.","DOI":"10.1109\/IISA.2015.7387995"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lin, Q., Zhang, D., Huang, X., Ni, H., and Zhou, X. (2012, January 5\u20137). Detecting Wandering Behavior Based on GPS Traces for Elders with Dementia. Proceedings of the 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV), Guangzhou, China.","DOI":"10.1109\/ICARCV.2012.6485238"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"123143","DOI":"10.1016\/j.eswa.2024.123143","article-title":"Human Activity Recognition with Smartphone-Integrated Sensors: A Survey","volume":"246","author":"Dentamaro","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sadri, A. (2016, January 14\u201318). Mining Changes in Mobility Patterns from Smartphone Data. Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Sydney, Australia.","DOI":"10.1109\/PERCOMW.2016.7457083"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bieber, G., Luthardt, A., Peter, C., and Urban, B. (2011, January 25\u201327). The Hearing Trousers Pocket: Activity Recognition by Alternative Sensors. Proceedings of the 4th International Conference on Pervasive Technologies Related to Assistive Environments, Heraklion, Crete, Greece.","DOI":"10.1145\/2141622.2141674"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Novak, M., Binas, M., and Jakab, F. (2012, January 21\u201322). Unobtrusive Anomaly Detection in Presence of Elderly in a Smart-Home Environment. Proceedings of the 2012 ELEKTRO, Rajeck Teplice, Slovakia.","DOI":"10.1109\/ELEKTRO.2012.6225617"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cejudo, A., Beristain, A., Almeida, A., Rebescher, K., Mart\u00edn, C., and Mac\u00eda, I. (2025). Smart Home-Assisted Anomaly Detection System for Older Adults: A Deep Learning Approach with a Comprehensive Set of Daily Activities. Med. Biol. Eng. Comput.","DOI":"10.1007\/s11517-025-03308-y"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s11257-015-9162-6","article-title":"Statistical Models for Unobtrusively Detecting Abnormal Periods of Inactivity in Older Adults","volume":"25","author":"Moshtaghi","year":"2015","journal-title":"User Model. User-Adap. Inter."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e28260","DOI":"10.2196\/28260","article-title":"Detecting Anomalies in Daily Activity Routines of Older Persons in Single Resident Smart Homes: Proof-of-Concept Study","volume":"5","author":"Shahid","year":"2022","journal-title":"JMIR Aging"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e45876","DOI":"10.2196\/45876","article-title":"Differences in Life Space Activity Patterns Between Older Adults with Mild Cognitive Impairment Living Alone or as a Couple: Cohort Study Using Passive Activity Sensing","volume":"6","author":"Muurling","year":"2023","journal-title":"JMIR Aging"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Susnea, I., Pecheanu, E., Sandu, C., and Cocu, A. (2021). A Scalable Solution to Detect Behavior Changes of Elderly People Living Alone. Appl. Sci., 12.","DOI":"10.3390\/app12010235"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wilhelm, S., and Wahl, F. (2024). Emergency Detection in Smart Homes Using Inactivity Score for Handling Uncertain Sensor Data. Sensors, 24.","DOI":"10.3390\/s24206583"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hu, R., Michel, B., Russo, D., Mora, N., Matrella, G., Ciampolini, P., Cocchi, F., Montanari, E., Nunziata, S., and Brunschwiler, T. (2020). An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care. Future Internet, 13.","DOI":"10.3390\/fi13010006"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Verstaevel, N., George, J.-P., Bernon, C., and Gleizes, M.-P. (2018, January 3\u20137). A Self-Organized Learning Model for Anomalies Detection: Application to Elderly People. Proceedings of the 2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), Trento, Italy.","DOI":"10.1109\/SASO.2018.00018"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kim, K., and Koshizuka, N. (2023, January 15\u201318). Extending Healthy Life Expectancy: Frailty Detection Model Using Smart Meter and Machine Learning. Proceedings of the 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy.","DOI":"10.1109\/BigData59044.2023.10386374"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Turimov Mustapoevich, D., and Kim, W. (2023). Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey. Healthcare, 11.","DOI":"10.3390\/healthcare11182483"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/RBME.2012.2222636","article-title":"The Role of Technology and Engineering Models in Transforming Healthcare","volume":"6","author":"Pavel","year":"2013","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"64006","DOI":"10.1109\/ACCESS.2024.3395051","article-title":"Anomaly Detection in Smart Environments: A Comprehensive Survey","volume":"12","author":"Damer","year":"2024","journal-title":"IEEE Access"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1126\/science.1177170","article-title":"Limits of Predictability in Human Mobility","volume":"327","author":"Song","year":"2010","journal-title":"Science"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/TETC.2022.3188939","article-title":"The Indoor Predictability of Human Mobility: Estimating Mobility with Smart Home Sensors","volume":"11","author":"Wang","year":"2023","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"ref_53","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":"Weakley","year":"2018","journal-title":"J. Biomed. Inform."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Gupta, D., Bhatt, S., Shokouhmand, S., and Faezipour, M. (2024). A Comprehensive Review of Machine Learning Approaches for Anomaly Detection in Smart Homes: Experimental Analysis and Future Directions. Future Internet, 16.","DOI":"10.3390\/fi16040139"},{"key":"ref_55","unstructured":"(2025, February 24). CASAS\u2014Center for Advanced Studies in Adaptive Systems, Washington State University Activity Recognition Datasets. Available online: https:\/\/data.casas.wsu.edu\/download\/."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"28219","DOI":"10.1109\/ACCESS.2022.3157726","article-title":"Activities Recognition, Anomaly Detection and Next Activity Prediction Based on Neural Networks in Smart Homes","volume":"10","author":"Alaghbari","year":"2022","journal-title":"IEEE Access"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Susnea, I., Dumitriu, L., Talmaciu, M., Pecheanu, E., and Munteanu, D. (2019). Unobtrusive Monitoring the Daily Activity Routine of Elderly People Living Alone, with Low-Cost Binary Sensors. Sensors, 19.","DOI":"10.3390\/s19102264"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.14778\/3538598.3538602","article-title":"Anomaly Detection in Time Series: A Comprehensive Evaluation","volume":"15","author":"Schmidl","year":"2022","journal-title":"Proc. VLDB Endow."},{"key":"ref_59","first-page":"56","article-title":"A Review on Outlier\/Anomaly Detection in Time Series Data","volume":"54","author":"Conde","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.neucom.2020.10.102","article-title":"Activity Recognition and Anomaly Detection in Smart Homes","volume":"423","author":"Fahad","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.artmed.2019.01.005","article-title":"Detection of Abnormal Behaviour for Dementia Sufferers Using Convolutional Neural Networks","volume":"94","author":"Arifoglu","year":"2019","journal-title":"Artif. Intell. Med."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/9\/2755\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:22:19Z","timestamp":1760030539000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/9\/2755"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,26]]},"references-count":61,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["s25092755"],"URL":"https:\/\/doi.org\/10.3390\/s25092755","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,26]]}}}