{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T21:15:08Z","timestamp":1772831708105,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,4,4]],"date-time":"2021-04-04T00:00:00Z","timestamp":1617494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["109-2221-E-024-012-MY3"],"award-info":[{"award-number":["109-2221-E-024-012-MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to the aging population, home care for the elderly has become very important. Currently, there are many studies focusing on the deployment of various sensors in the house to recognize the home activities of the elderly, especially for the elderly living alone. Through these, we can detect the home situation of the single person and ensure his\/her living safety. However, the living environment of the elderly includes, not only the person living alone, but also multiple people living together. By applying the traditional methods for a multi-resident environment, the \u201cindividual\u201d activities of each person could not be accurately identified. This resulted in an inability to distinguish which person was involved in what activities, and thus, failed to provide personal care. Therefore, this research tries to investigate how to recognize home activities in multi-resident living environments, in order to accurately distinguish the association between residents and home activities. Specifically, we propose to use the special characteristics of historical activity of residents in a multi-person environment, including activity interaction, activity frequency, activity period length, and residential behaviors, and then apply a suite of machine learning methods to train and test. Five traditional models of supervised learning and two deep learning methods are explored to tackle this problem. Through the experiments with real datasets, the proposed methods were found to achieve higher precision, recall and accuracy with less training time. The best accuracy can reach up to 91% and 95%, by J48DT, and LSTM, respectively, in different living environments.<\/jats:p>","DOI":"10.3390\/s21072520","type":"journal-article","created":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T11:48:29Z","timestamp":1617623309000},"page":"2520","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Applying Machine Learning Technologies Based on Historical Activity Features for Multi-Resident Activity Recognition"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8952-3577","authenticated-orcid":false,"given":"Jia-Ming","family":"Liang","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, National University of Tainan, Tainan 70005, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping-Lin","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 33302, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi-Jyun","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 33302, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0745-8394","authenticated-orcid":false,"given":"Shashank","family":"Mishra","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National University of Tainan, Tainan 70005, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.comcom.2020.08.017","article-title":"Deep learning for intelligent IoT: Opportunities, challenges and solutions","volume":"164","author":"Zikria","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pereira, C., Mesquita, J., Guimar\u00e3es, D., Santos, F., Almeida, L., and Aguiar, A. (2019). Open IoT Architecture for continuous patient monitoring in emergency wards. Electronics, 8.","DOI":"10.3390\/electronics8101074"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hassan, S.R., Ahmad, I., Ahmad, S., Alfaify, A., and Shafiq, M. (2020). Remote pain monitoring using fog computing for e-Healthcare: An efficient architecture. Sensors, 20.","DOI":"10.3390\/s20226574"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sampath, P., Packiriswamy, G., Kumar, N.P., Shanmuganathan, V., Song, O.-Y., Tariq, U., and Nawaz, R. (2020). IoT based health-Related topic recognition from emerging online health community (Med Help) using machine learning technique. Electronics, 9.","DOI":"10.3390\/electronics9091469"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2196\/20215","article-title":"Using ambient assisted living to monitor older adults with alzheimer disease: Single-case study to validate the monitoring report","volume":"8","author":"Lussier","year":"2020","journal-title":"JMIR Med. Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2018.02.010","article-title":"Deep learning for sensor-based activity recognition: A survey","volume":"119","author":"Wang","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1109\/JBHI.2019.2918412","article-title":"Efficient activity recognition in smart homes using delayed fuzzy temporal windows on binary sensors","volume":"24","author":"Hamad","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Thapa, K., Abdullah, A., Lamichhane, Z.M., and Yang, S.-H. (2020). A deep machine learning method for concurrent and interleaved human activity recognition. Sensors, 20.","DOI":"10.3390\/s20205770"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2567","DOI":"10.1007\/s42835-019-00278-8","article-title":"Vision-based human activity recognition system using depth silhouettes: A smart home system for monitoring the residents","volume":"14","author":"Kim","year":"2019","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_10","first-page":"1","article-title":"Vision-based human action recognition: An overview and real world challenges","volume":"32","author":"Jeghama","year":"2020","journal-title":"Forensic Sci. Int. Digit. Investig."},{"key":"ref_11","first-page":"1","article-title":"Privacyeye: A privacy-preserving and computationally efficient deep learning-based mobile video analytics system","volume":"1","author":"Du","year":"2021","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hu, N., Su, S., Tang, C., and Wang, L. (2020). Wearable-sensors based activity recognition for smart human healthcare using internet of things. Int. Wirel. Commun. Mob. Comput., 1909\u20131915.","DOI":"10.1109\/IWCMC48107.2020.9148197"},{"key":"ref_13","unstructured":"Malas\u00e9, A., Maurice, P., Colas, F., Charpillet, F., and Ivaldi, S. (2018, January 25\u201329). Activity recognition with multiple wearable sensors for industrial applications. Proceedings of the the Eleventh International Conference on Advances in Computer-Human Interactions, Rome, Italy."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"155060","DOI":"10.1109\/ACCESS.2020.3017681","article-title":"Deep human activity recognition with localisation of wearable sensors","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1007\/s11036-019-01445-x","article-title":"Deep learning models for real-time human activity recognition with smartphones","volume":"25","author":"Wan","year":"2020","journal-title":"Mob. Netw. Appl."},{"key":"ref_16","first-page":"1","article-title":"CARIN: Wireless CSI-based driver activity recognition under the interference of passengers","volume":"4","author":"Bai","year":"2020","journal-title":"ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_17","unstructured":"(2021, February 15). Aging Project: Living Together with Your Friends. Available online: http:\/\/www.activageproject.eu\/blog\/2020\/02\/11\/Aging-Project-Living-together-with-your-friends\/."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Arrigoitia, M.F., and West, K. (2020). Interdependence, commitment, learning and love. The case of the United Kingdom\u2032s first older women\u2019s co-housing community. Ageing Soc., 1\u201335.","DOI":"10.1017\/S0144686X19001673"},{"key":"ref_19","unstructured":"D\u00fchr, S. (2020). The role of government in supporting co-housing accommodation models for older people in Germany: Towards a research agenda. Univ. S. Aust., 1\u201335."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mohamed, R., Perumal, T., Sulaiman, M.N., Mustapha, N., and Zainudin, M.N.S. (2017). Modeling activity recognition of multi resident using label combination of multilabel classification in smart home. AIP Conf. Proc., 1\u20137.","DOI":"10.1063\/1.5005427"},{"key":"ref_21","unstructured":"Rogelj, V., and Bogataj, D. (July, January 29). Ambient assisted living technologies and environments: Literature review and research agenda. Proceedings of the International Conference on Control, Decision and Information Technologies, Prague, Czech Republic."},{"key":"ref_22","first-page":"1","article-title":"AnAbEL: Towards empowering people living with dementia in ambient assisted living","volume":"1","author":"Manuel","year":"2020","journal-title":"Univers. Access Inf. Soc."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Taylor, W., Shah, S.A., Dashtipour, K., Zahid, A., Abbasi, Q.H., and Imran, M.A. (2020). An intelligent non-invasive real-time human activity recognition system for next-generation healthcare. Sensors, 20.","DOI":"10.3390\/s20092653"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Alemdar, H., Ertan, H., and Ersoy, O.D.I.C. (2013, January 5\u20138). ARAS human activity datasets in multiple homes with multiple residents. Proceedings of the International Conference on Pervasive Computing Technologies for Healthcare and Workshops, Venice, Italy.","DOI":"10.4108\/pervasivehealth.2013.252120"},{"key":"ref_25","unstructured":"Shalabi, L.A., and Shaaban, Z. (July, January 27). Normalization as a preprocessing engine for data mining and the approach of preference matrix. Proceedings of the International Conference on Dependability of Complex Systems, Wroc\u0142aw, Poland."},{"key":"ref_26","first-page":"5","article-title":"Support vector machines for classification and regression","volume":"14","author":"Gunn","year":"1998","journal-title":"ISIS Tech. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4249\/scholarpedia.1883","article-title":"K-nearest neighbor","volume":"4","author":"Peterson","year":"2009","journal-title":"Scholarpedia"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/72.991427","article-title":"A comparison of methods for multiclass support vector machines","volume":"13","author":"Hsu","year":"2002","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/S0169-7439(03)00094-7","article-title":"A comparative study of k-nearest neighbor, support vector machine and multi-layer perceptron for thalassemia screening","volume":"69","author":"Amendolia","year":"2013","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_30","first-page":"1","article-title":"Distance metric learning for large margin nearest neighbor classification","volume":"10","author":"Weinberger","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref_31","first-page":"18","article-title":"Classification and regression by random forest","volume":"2","author":"Liaw","year":"2002","journal-title":"R. News"},{"key":"ref_32","first-page":"13","article-title":"A comparative study of decision tree ID3 and C4.5","volume":"4","author":"Hssina","year":"2014","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1023\/A:1007607513941","article-title":"An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization","volume":"4","author":"Dietterich","year":"2000","journal-title":"Mach. Learn."},{"key":"ref_34","first-page":"1","article-title":"Decision tree analysis on J48 algorithm for data mining","volume":"3","author":"Bhargava","year":"2013","journal-title":"Int. J. Adv. Res. Comput. Sci. Softw. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1109\/72.279188","article-title":"Recurrent neural networks and robust time series prediction","volume":"5","author":"Connor","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_36","unstructured":"Olah, C. (2015). Understanding LSTM networks. Google Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s11222-009-9153-8","article-title":"Estimation of prediction error by using K-fold cross-validation","volume":"21","author":"Fushiki","year":"2011","journal-title":"Stat. Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2520\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:33:49Z","timestamp":1760362429000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2520"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,4]]},"references-count":37,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["s21072520"],"URL":"https:\/\/doi.org\/10.3390\/s21072520","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,4]]}}}