{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T04:57:44Z","timestamp":1777611464207,"version":"3.51.4"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T00:00:00Z","timestamp":1643241600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006595","name":"Unitatea Executiva Pentru Finantarea Invatamantului Superior a Cercetarii Dezvoltarii si Inovarii","doi-asserted-by":"publisher","award":["AAL159\/2020 H2HCare"],"award-info":[{"award-number":["AAL159\/2020 H2HCare"]}],"id":[{"id":"10.13039\/501100006595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006595","name":"Unitatea Executiva Pentru Finantarea Invatamantului Superior a Cercetarii Dezvoltarii si Inovarii","doi-asserted-by":"publisher","award":["AAL59\/2018 Remind"],"award-info":[{"award-number":["AAL59\/2018 Remind"]}],"id":[{"id":"10.13039\/501100006595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006595","name":"Unitatea Executiva Pentru Finantarea Invatamantului Superior a Cercetarii Dezvoltarii si Inovarii","doi-asserted-by":"publisher","award":["AAL264\/2021 engAGE"],"award-info":[{"award-number":["AAL264\/2021 engAGE"]}],"id":[{"id":"10.13039\/501100006595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006595","name":"Unitatea Executiva Pentru Finantarea Invatamantului Superior a Cercetarii Dezvoltarii si Inovarii","doi-asserted-by":"publisher","award":["AAL162\/2020 ReMember-Me"],"award-info":[{"award-number":["AAL162\/2020 ReMember-Me"]}],"id":[{"id":"10.13039\/501100006595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As the population in the Western world is rapidly aging, the remote monitoring solutions integrated into the living environment of seniors have the potential to reduce the care burden helping them to self-manage problems associated with old age. The daily routine is considered a useful tool for addressing age-related problems having additional benefits for seniors like reduced stress and anxiety, increased feeling of safety and security. In this paper, we propose a solution for identifying the daily routines of seniors using the monitored activities of daily living and for inferring deviations from the routines that may require caregivers\u2019 interventions. A Markov model-based method is defined to identify the daily routines, while entropy rate and cosine functions are used to measure and assess the similarity between the daily monitored activities in a day and the inferred routine. A distributed monitoring system was developed that uses Beacons and trilateration techniques for monitoring the activities of older adults. The results are promising, the proposed techniques can identify the daily routines with confidence concerning the activity duration of 0.98 and the sequence of activities in the interval of [0.0794, 0.0829]. Regarding deviation identification, our method obtains 0.88 as the best sensitivity value with an average precision of 0.95.<\/jats:p>","DOI":"10.3390\/s22030992","type":"journal-article","created":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T22:01:57Z","timestamp":1643320917000},"page":"992","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Identifying and Monitoring the Daily Routine of Seniors Living at Home"],"prefix":"10.3390","volume":"22","author":[{"given":"Viorica Rozina","family":"Chifu","sequence":"first","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3194-080X","authenticated-orcid":false,"given":"Cristina Bianca","family":"Pop","sequence":"additional","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7440-2235","authenticated-orcid":false,"given":"David","family":"Demjen","sequence":"additional","affiliation":[{"name":"Department of Informatics, Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Radu","family":"Socaci","sequence":"additional","affiliation":[{"name":"Mobile Clients Team, Prime Video, Amazon, 1 Principal Place, Worship St, London EC2A 2FA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Todea","sequence":"additional","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2998-4176","authenticated-orcid":false,"given":"Marcel","family":"Antal","sequence":"additional","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tudor","family":"Cioara","sequence":"additional","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6166-5266","authenticated-orcid":false,"given":"Ionut","family":"Anghel","sequence":"additional","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4886-3572","authenticated-orcid":false,"given":"Claudia","family":"Antal","sequence":"additional","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,27]]},"reference":[{"key":"ref_1","unstructured":"(2021, December 15). Ageing Europe\u2014Statistics on Population Developments. Available online: https:\/\/ec.europa.eu\/eurostat\/statistics-explained\/index.php?title=Ageing_Europe_-_statistics_on_population_developments."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Vos, E.E., de Bruin, S.R., van der Beek, A.J., and Proper, K.I. (2021). \u201cIt\u2019s Like Juggling, Constantly Trying to Keep All Balls in the Air\u201d: A Qualitative Study of the Support Needs of Working Caregivers Taking Care of Older Adults. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18115701"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"\u0160are, S., Ljubi\u010di\u0107, M., Gusar, I., \u010canovi\u0107, S., and Konjevoda, S. (2021). Self-Esteem, Anxiety, and Depression in Older People in Nursing Homes. Healthcare, 9.","DOI":"10.3390\/healthcare9081035"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1038\/s41398-020-0697-x","article-title":"Delaying memory decline: Different options and emerging solutions","volume":"10","author":"Schneider","year":"2020","journal-title":"Transl. Psychiatry"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Anghel, I., Cioara, T., Moldovan, D., Antal, C., Pop, C.D., Salomie, I., Pop, C.B., and Chifu, V.R. (2020). Smart Environments and Social Robots for Age-Friendly Integrated Care Services. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17113801"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.ergon.2018.02.002","article-title":"A survey on health monitoring systems for health smart homes","volume":"66","author":"Mshali","year":"2018","journal-title":"Int. J. Ind. Ergon."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zekri, D., Delot, T., Thilliez, M., LeComte, S., and Desertot, M. (2020). A Framework for Detecting and Analyzing Behavior Changes of Elderly People over Time Using Learning Techniques. Sensors, 20.","DOI":"10.3390\/s20247112"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1109\/COMST.2019.2948204","article-title":"A Survey on Anomalous Behavior Detection for Elderly Care Using Dense-Sensing Networks","volume":"22","author":"Deep","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Janjua, Z.H., Riboni, D., and Bettini, C. (2016, January 4\u20138). Towards automatic induction of abnormal behavioral patterns for recognizing mild cognitive impairment. Proceedings of the 31st Annual ACM Symposium on Applied Computing, Pisa, Italy.","DOI":"10.1145\/2851613.2851687"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.artmed.2015.12.001","article-title":"SmartFABER: Recognizing Fine-Grained Abnormal Behaviors for Early Detection of Mild Cognitive Impairment","volume":"67","author":"Riboni","year":"2016","journal-title":"Artif. Intell. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s00779-014-0820-1","article-title":"Sensor-based Bayesian detection of anomalous living patterns in a home setting","volume":"19","author":"Sanchis","year":"2015","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Casagrande, F.D., and Zouganeli, E. (2019). Activity Recognition and Prediction in Real Homes. Nordic Artificial Intelligence Research and Development. NAIS 2019: Communications in Computer and Information Science, Springer.","DOI":"10.1007\/978-3-030-35664-4_2"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4560365","DOI":"10.1155\/2016\/4560365","article-title":"Two-Layer Hidden Markov Model for Human Activity Recognition in Home Environments","volume":"12","author":"Kabir","year":"2016","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12652-015-0294-7","article-title":"Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments","volume":"7","author":"Roy","year":"2016","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.jbi.2018.07.006","article-title":"A sequence-to-sequence model-based deep learning approach for recognizing activity of daily living for senior care","volume":"84","author":"Zhu","year":"2018","journal-title":"J. Biomed. Inform."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F.J., and Roggen, D. (2016). Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1109\/TASE.2015.2474743","article-title":"Wearable Sensor-Based Behavioral Anomaly Detection in Smart Assisted Living Systems","volume":"12","author":"Zhu","year":"2015","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104717","DOI":"10.1016\/j.dib.2019.104717","article-title":"A dataset build using wearable inertial measurement and ECG sensors for activity recognition, fall detection and basic heart anomaly detection system","volume":"27","author":"Nadeem","year":"2019","journal-title":"Data Brief"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"173428","DOI":"10.1109\/ACCESS.2020.3025808","article-title":"Machine Learning-Based Automatic Detection of Central Sleep Apnea Events from a Pressure Sensitive Mat","volume":"8","author":"Azimi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"78658","DOI":"10.1109\/ACCESS.2021.3083060","article-title":"Machine Learning for Anomaly Detection: A Systematic Review","volume":"9","author":"Nassif","year":"2021","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"453","DOI":"10.3233\/AIS-190536","article-title":"Personalized real-time anomaly detection and health feedback for older adults","volume":"11","author":"Parvin","year":"2019","journal-title":"J. Ambient Intell. Smart Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42979-021-00744-z","article-title":"Machine Learning-Based Anxiety Detection in Older Adults Using Wristband Sensors and Context Feature","volume":"2","author":"Nath","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Parvin, P., Paterno, F., and Chessa, S. (2018, January 25\u201328). Anomaly Detection in the Elderly Daily Behavior. Proceedings of the 2018 14th International Conference on Intelligent Environments (IE), Rome, Italy.","DOI":"10.1109\/IE.2018.00025"},{"key":"ref_24","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."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Zerkouk, M., and Chikhaoui, B. (2020). Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models. Sensors, 20.","DOI":"10.3390\/s20082359"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1016\/j.patcog.2014.07.007","article-title":"A Context-Aware Ap-proach for Long-Term Behavioural Change Detection and Abnormality Prediction in Ambient Assisted Living","volume":"48","author":"Forkan","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Eisa, S., and Moreira, A. (2017). A Behaviour Monitoring System (BMS) for Ambient Assisted Living. Sensors, 17.","DOI":"10.3390\/s17091946"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Aran, O., Sanchez-Cortes, D., Do, M.-T., and Gatica-Perez, D. Anomaly Detection in Elderly Daily Behavior in Ambient Sensing Environments. International Workshop on Human Behavior Understanding, HBU 2016: Human Behavior Understanding, Springer.","DOI":"10.1007\/978-3-319-46843-3_4"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.eswa.2016.02.030","article-title":"Detecting and exploring deviating behaviour of smart home residents","volume":"55","author":"Verikas","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_32","first-page":"1","article-title":"Automatic Extraction of Behavioral Patterns for Elderly Mobility and Daily Routine Analysis","volume":"9","author":"Li","year":"2018","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, S., Li, W., and Cao, J. (2018). A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution. Sensors, 18.","DOI":"10.3390\/s18061850"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Alshammari, T., Alshammari, N., Sedky, M., and Howard, C. (2018). SIMADL: Simulated Activities of Daily Living Dataset. Data, 3.","DOI":"10.3390\/data3020011"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Vavoulas, G., Chatzaki, C., Malliotakis, T., Pediaditis, M., and Tsiknakis, M. (2016, January 21\u201322). The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones. Proceedings of the 2nd International Conference on Infor-mation and Communication Technologies for Ageing Well and e-Health (ICT4AWE), Rome, Italy.","DOI":"10.5220\/0005792401430151"},{"key":"ref_36","first-page":"1727","article-title":"Detecting deviations from activities of daily living routines using kinect depth maps and power consumption data","volume":"11","author":"Armstrong","year":"2019","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"445","DOI":"10.3233\/AIS-180505","article-title":"Towards context-aware assistive applications for aging in place via real-life-proof activity detection","volume":"10","author":"Caroux","year":"2018","journal-title":"J. Ambient Intell. Smart Environ."},{"key":"ref_38","unstructured":"Paudel, R., Eberle, W., and Holder, L.B. (August, January 30). Anomaly Detection of Elderly Patient Activities in Smart Homes using a Graph-Based Approach. Proceedings of the International Conference of Data Science, Las Vegas, NV, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.pmcj.2015.09.007","article-title":"Modeling patterns of activities using activity curves","volume":"28","author":"Dawadi","year":"2016","journal-title":"Pervasive Mob. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Manca, M., Parvin, P., Patern\u00f2, F., and Santoro, C. (2017, January 26\u201329). Detecting Anomalous Elderly Behaviour in Ambient Assisted Living. Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems, Lisbon, Portugal.","DOI":"10.1145\/3102113.3102128"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Qin, S.M., Verkasalo, H., Mohtaschemi, M., Hartonen, T., and Alava, M. (2012). Patterns, entropy, and predictability of human mobility and life. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0051353"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Porta, A., Valencia, J.F., Cairo, B., Bari, V., De Maria, B., Gelpi, F., Barbic, F., and Furlan, R. (2020). Are Strategies Favoring Pattern Matching a Viable Way to Improve Complexity Estimation Based on Sample Entropy?. Entropy, 22.","DOI":"10.3390\/e22070724"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Marki\u0107, I., \u0160tula, M., Zori\u0107, M., and Stipani\u010dev, D. (2020). Entropy-Based Approach in Selection Exact String-Matching Algorithms. Entropy, 23.","DOI":"10.3390\/e23010031"},{"key":"ref_44","unstructured":"Chan, A.C., and Chung, R.M. (2021). Security and Privacy of Wireless Beacon Systems. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/MCOM.2014.6710070","article-title":"From \u201csmart objects\u201d to \u201csocial objects\u201d: The next evolutionary step of the internet of things","volume":"52","author":"Atzori","year":"2014","journal-title":"IEEE Commun. Mag."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Bilbao-Jayo, A., Almeida, A., Sergi, I., Montanaro, T., Fasano, L., Emaldi, M., and Patrono, L. (2021). Behavior Modeling for a Beacon-Based Indoor Location System. Sensors, 21.","DOI":"10.3390\/s21144839"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, J., Yue, X., Chen, J., and Deng, F. (2017). A Novel Robust Trilateration Method Applied to Ultra-Wide Bandwidth Location Systems. Sensors, 17.","DOI":"10.3390\/s17040795"},{"key":"ref_48","first-page":"385","article-title":"3D Trilateration Localization using RSSI in Indoor Environment","volume":"11","author":"Rose","year":"2020","journal-title":"Int. J. Adv. Comput. Sci. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/992\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:09:03Z","timestamp":1760134143000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/992"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,27]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22030992"],"URL":"https:\/\/doi.org\/10.3390\/s22030992","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,27]]}}}