{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T15:19:30Z","timestamp":1778599170040,"version":"3.51.4"},"reference-count":50,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,4,19]],"date-time":"2019-04-19T00:00:00Z","timestamp":1555632000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Human Activity Recognition (HAR) is the process of automatically detecting human actions from the data collected from different types of sensors. Research related to HAR has devoted particular attention to monitoring and recognizing the human activities of a single occupant in a home environment, in which it is assumed that only one person is present at any given time. Recognition of the activities is then used to identify any abnormalities within the routine activities of daily living. Despite the assumption in the published literature, living environments are commonly occupied by more than one person and\/or accompanied by pet animals. In this paper, a novel method based on different entropy measures, including Approximate Entropy (ApEn), Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn), is explored to detect and identify a visitor in a home environment. The research has mainly focused on when another individual visits the main occupier, and it is, therefore, not possible to distinguish between their movement activities. The goal of this research is to assess whether entropy measures can be used to detect and identify the visitor in a home environment. Once the presence of the main occupier is distinguished from others, the existing activity recognition and abnormality detection processes could be applied for the main occupier. The proposed method is tested and validated using two different datasets. The results obtained from the experiments show that the proposed method could be used to detect and identify a visitor in a home environment with a high degree of accuracy based on the data collected from the occupancy sensors.<\/jats:p>","DOI":"10.3390\/e21040416","type":"journal-article","created":{"date-parts":[[2019,4,22]],"date-time":"2019-04-22T03:15:53Z","timestamp":1555902953000},"page":"416","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Exploring Entropy Measurements to Identify Multi-Occupancy in Activities of Daily Living"],"prefix":"10.3390","volume":"21","author":[{"given":"Aadel","family":"Howedi","sequence":"first","affiliation":[{"name":"School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5139-6565","authenticated-orcid":false,"given":"Ahmad","family":"Lotfi","sequence":"additional","affiliation":[{"name":"School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7737-1393","authenticated-orcid":false,"given":"Amir","family":"Pourabdollah","sequence":"additional","affiliation":[{"name":"School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.pmcj.2015.04.003","article-title":"Monitoring elderly behavior via indoor position-based stigmergy","volume":"23","author":"Barsocchi","year":"2015","journal-title":"Pervasive Mob. Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10734","DOI":"10.1109\/ACCESS.2017.2711495","article-title":"Front-door event classification algorithm for elderly people living alone in smart house using wireless binary sensors","volume":"5","author":"Tan","year":"2017","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Khan, S.S., Karg, M.E., Hoey, J., and Kulic, D. (2012, January 5\u20138). Towards the detection of unusual temporal events during activities using hmms. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA.","DOI":"10.1145\/2370216.2370444"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.pmcj.2016.05.003","article-title":"Unsupervised visit detection in smart homes","volume":"34","author":"Aicha","year":"2017","journal-title":"Pervasive Mob. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s12652-016-0440-x","article-title":"Multi-resident activity tracking and recognition in smart environments","volume":"8","author":"Alemdar","year":"2017","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_6","first-page":"39","article-title":"Tracking and Recognizing the Activity of Multi Resident in Smart Home Environments","volume":"9","author":"Mohamed","year":"2017","journal-title":"J. Telecommun. Electron. Comput. Eng. (JTEC)"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.neucom.2016.05.110","article-title":"Modeling interaction in multi-resident activities","volume":"230","author":"Benmansour","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Emi, I.A., and Stankovic, J.A. (2015, January 14\u201316). SARRIMA: Smart ADL recognizer and resident identifier in multi-resident accommodations. Proceedings of the Conference on Wireless Health, Bethesda, MD, USA.","DOI":"10.1145\/2811780.2811916"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.neucom.2018.08.033","article-title":"Recognizing multi-resident activities in non-intrusive sensor-based smart homes by formal concept analysis","volume":"318","author":"Hao","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mokhtari, G., Anvari-Moghaddam, A., Zhang, Q., and Karunanithi, M. (2018). Multi-Residential Activity Labelling in Smart Homes with Wearable Tags Using BLE Technology. Sensors, 18.","DOI":"10.3390\/s18030908"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1145\/2835372","article-title":"Multioccupant activity recognition in pervasive smart home environments","volume":"48","author":"Benmansour","year":"2016","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/ACCESS.2017.2685531","article-title":"An Efficient Activity Recognition Framework: Toward Privacy-Sensitive Health Data Sensing","volume":"5","author":"Samarah","year":"2017","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kim, Y., An, J., Lee, M., and Lee, Y. (2017, January 29\u201331). An Activity-Embedding Approach for Next-Activity Prediction in a Multi-User Smart Space. Proceedings of the 2017 IEEE International Conference on Smart Computing (SMARTCOMP), Hong Kong, China.","DOI":"10.1109\/SMARTCOMP.2017.7946985"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2184","DOI":"10.3390\/e16042184","article-title":"A two-stage method for solving multi-resident activity recognition in smart environments","volume":"16","author":"Chen","year":"2014","journal-title":"Entropy"},{"key":"ref_15","unstructured":"Benmansour, A. (2016). Human Activity Recognition in the Context of Multi-Occupant Smart Homes. [Ph.D. Thesis, University of Abou Bekr Belka\u00efd]."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Crandall, A.S., and Cook, D.J. (2010, January 19\u201321). Using a hidden markov model for resident identification. Proceedings of the 2010 Sixth International Conference on Intelligent Environments, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IE.2010.21"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tran, S.N., Zhang, Q., and Karunanithi, M. (2018). On Multi-resident Activity Recognition in Ambient Smart-Homes. arXiv.","DOI":"10.1109\/PERCOMW.2018.8480132"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gu, T., Wu, Z., Wang, L., Tao, X., and Lu, J. (2009, January 13\u201316). Mining emerging patterns for recognizing activities of multiple users in pervasive computing. Proceedings of the 2009 6th Annual International Mobile and Ubiquitous Systems: Networking & Services, MobiQuitous, Toronto, ON, Canada.","DOI":"10.4108\/ICST.MOBIQUITOUS2009.6818"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.pmcj.2010.11.008","article-title":"Recognizing multi-user activities using wearable sensors in a smart home","volume":"7","author":"Wang","year":"2011","journal-title":"Pervasive Mob. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1109\/TAC.1979.1102177","article-title":"An algorithm for tracking multiple targets","volume":"24","author":"Reid","year":"1979","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"31314","DOI":"10.3390\/s151229858","article-title":"Physical human activity recognition using wearable sensors","volume":"15","author":"Attal","year":"2015","journal-title":"Sensors"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hsu, K.C., Chiang, Y.T., Lin, G.Y., Lu, C.H., Hsu, J.Y.J., and Fu, L.C. (2010, January 1\u20134). Strategies for inference mechanism of conditional random fields for multiple-resident activity recognition in a smart home. Proceedings of the International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, C\u00f3rdoba, Spain.","DOI":"10.1007\/978-3-642-13022-9_42"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1080\/01969720903584183","article-title":"Detection of social interaction in smart spaces","volume":"41","author":"Cook","year":"2010","journal-title":"Cybern. Syst. Int. J."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Prossegger, M., and Bouchachia, A. (2014). Multi-resident activity recognition using incremental decision trees. Adaptive and Intelligent Systems, Springer.","DOI":"10.1007\/978-3-319-11298-5_19"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Alemdar, H., Ertan, H., Incel, O.D., and Ersoy, C. (2013, January 5\u20138). ARAS human activity datasets in multiple homes with multiple residents. Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare, Venice, Italy.","DOI":"10.4108\/pervasivehealth.2013.252120"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9692","DOI":"10.3390\/s140609692","article-title":"Multimodal wireless sensor network-based ambient assisted living in real homes with multiple residents","volume":"14","author":"Tunca","year":"2014","journal-title":"Sensors"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhu, C., Cheng, Q., and Sheng, W. (2010, January 7\u201310). Human activity recognition via motion and vision data fusion. Proceedings of the 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.2010.5757529"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1117\/12.591950","article-title":"Optimization of transform coefficient selection and motion vector estimation considering interpicture dependencies in hybrid video coding","volume":"Volume 5685","author":"Schumitsch","year":"2005","journal-title":"Image and Video Communications and Processing 2005"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/JOE.1983.1145560","article-title":"Sonar tracking of multiple targets using joint probabilistic data association","volume":"8","author":"Fortmann","year":"1983","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_31","unstructured":"Han, M., Xu, W., Tao, H., and Gong, Y. (July, January 27). An algorithm for multiple object trajectory tracking. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA."},{"key":"ref_32","unstructured":"Oh, S., Russell, S., and Sastry, S. (2004, January 14\u201317). Markov chain Monte Carlo data association for general multiple-target tracking problems. Proceedings of the 2004 43rd IEEE Conference on Decision and Control (CDC), Nassau, Bahamas."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Petersen, J., Larimer, N., Kaye, J.A., Pavel, M., and Hayes, T.L. (September, January 28). SVM to detect the presence of visitors in a smart home environment. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA.","DOI":"10.1109\/EMBC.2012.6347324"},{"key":"ref_34","unstructured":"Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective, MIT Press."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press.","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Roy, N., Misra, A., and Cook, D. (2013, January 18\u201322). Infrastructure-assisted smartphone-based ADL recognition in multi-inhabitant smart environments. Proceedings of the 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom), San Diego, CA, USA.","DOI":"10.1109\/PerCom.2013.6526712"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"21","DOI":"10.14257\/ijsh.2017.11.6.03","article-title":"Multi Resident Complex Activity Recognition in Smart Home: A Literature Review","volume":"11","author":"Mohamed","year":"2017","journal-title":"Int. J. Smart Home"},{"key":"ref_38","unstructured":"(2019, April 18). W.S. University: CASAS Smart Home Project. Available online: http:\/\/ailab.eecs.wsu.edu\/casas\/."},{"key":"ref_39","unstructured":"Chiang, Y.T., Hsu, K.C., Lu, C.H., Fu, L.C., and Hsu, J.Y.J. (2010, January 18\u201322). Interaction models for multiple-resident activity recognition in a smart home. Proceedings of the 2010 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan."},{"key":"ref_40","unstructured":"R\u00e9nyi, A. (1961). On measures of entropy and information. Contributions to the Theory of Statistics, Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, USA, 20\u201330 July 1960, University of California Press."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.medengphy.2008.04.005","article-title":"Measuring complexity using fuzzyen, apen, and sampen","volume":"31","author":"Chen","year":"2009","journal-title":"Med. Eng. Phys."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.bspc.2017.05.002","article-title":"Multiple entropies performance measure for detection and localization of multi-channel epileptic EEG","volume":"38","author":"Tibdewal","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1073\/pnas.88.6.2297","article-title":"Approximate entropy as a measure of system complexity","volume":"88","author":"Pincus","year":"1991","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol.-Heart Circ. Physiol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Li, W., Ming, D., Xu, R., Ding, H., Qi, H., and Wan, B. (2012, January 26\u201331). Research on visual attention classification based on EEG entropy parameters. Proceedings of the World Congress on Medical Physics and Biomedical Engineering, Beijing, China.","DOI":"10.1007\/978-3-642-29305-4_408"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/TNSRE.2007.897025","article-title":"Characterization of surface EMG signal based on fuzzy entropy","volume":"15","author":"Chen","year":"2007","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_47","unstructured":"Realgames (2018, June 24). HOME I\/O Smart Home Automation Simulation. Available online: https:\/\/realgames.co\/home-io\/."},{"key":"ref_48","unstructured":"Dheeru, D., and Karra Taniskidou, E. (2019, April 18). UCI Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu\/ml\/datasets\/Activities+of+Daily+Living+%28ADLs%29+Recognition+Using+Binary+Sensors."},{"key":"ref_49","unstructured":"(2018, July 30). Time Series Entropy Measures Implemented With Numpy. Available online: https:\/\/github.com\/ixjlyons\/entro-py\/blob\/master\/entropy.py."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chernbumroong, S., Lotfi, A., and Langensiepen, C. (2014, January 9\u201312). Prediction of mobility entropy in an Ambient Intelligent environment. Proceedings of the 2014 IEEE Symposium on Intelligent Agents (IA), Orlando, FL, USA.","DOI":"10.1109\/IA.2014.7009460"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/4\/416\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:46:42Z","timestamp":1760186802000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/4\/416"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,19]]},"references-count":50,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["e21040416"],"URL":"https:\/\/doi.org\/10.3390\/e21040416","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,19]]}}}