{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T16:18:11Z","timestamp":1780071491680,"version":"3.54.0"},"reference-count":63,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,7,30]],"date-time":"2020-07-30T00:00:00Z","timestamp":1596067200000},"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>This paper presents anomaly detection in activities of daily living based on entropy measures. It is shown that the proposed approach will identify anomalies when there are visitors representing a multi-occupant environment. Residents often receive visits from family members or health care workers. Therefore, the residents\u2019 activity is expected to be different when there is a visitor, which could be considered as an abnormal activity pattern. Identifying anomalies is essential for healthcare management, as this will enable action to avoid prospective problems early and to improve and support residents\u2019 ability to live safely and independently in their own homes. Entropy measure analysis is an established method to detect disorder or irregularities in many applications: however, this has rarely been applied in the context of activities of daily living. An experimental evaluation is conducted to detect anomalies obtained from a real home environment. Experimental results are presented to demonstrate the effectiveness of the entropy measures employed in detecting anomalies in the resident\u2019s activity and identifying visiting times in the same environment.<\/jats:p>","DOI":"10.3390\/e22080845","type":"journal-article","created":{"date-parts":[[2020,7,31]],"date-time":"2020-07-31T04:15:31Z","timestamp":1596168931000},"page":"845","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["An Entropy-Based Approach for Anomaly Detection in Activities of Daily Living in the Presence of a Visitor"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7586-9737","authenticated-orcid":false,"given":"Aadel","family":"Howedi","sequence":"first","affiliation":[{"name":"School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1016\/j.eswa.2012.09.004","article-title":"Elderly activities recognition and classification for applications in assisted living","volume":"40","author":"Chernbumroong","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_2","first-page":"8","article-title":"Estimating the changing population of the \u2018oldest old\u2019","volume":"132","author":"Dini","year":"2008","journal-title":"Popul. Trends"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"11312","DOI":"10.3390\/s150511312","article-title":"The elderly\u2019s independent living in smart homes: A characterization of activities and sensing infrastructure survey to facilitate services development","volume":"15","author":"Ni","year":"2015","journal-title":"Sensors"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Guo, J., Li, Y., Hou, M., Han, S., and Ren, J. (2020). Recognition of Daily Activities of Two Residents in a Smart Home Based on Time Clustering. Sensors, 20.","DOI":"10.3390\/s20051457"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Subasi, A., Khateeb, K., Brahimi, T., and Sarirete, A. (2020). Human activity recognition using machine learning methods in a smart healthcare environment. Innovation in Health Informatics, Elsevier.","DOI":"10.1016\/B978-0-12-819043-2.00005-8"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"9718","DOI":"10.1109\/JSEN.2018.2866806","article-title":"Multi-Resident Activity Recognition in a Smart Home Using RGB Activity Image and DCNN","volume":"18","author":"Tan","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_8","unstructured":"Naser, A., Lotfi, A., Zhong, J., and He, J. (July, January 30). Human activity of daily living recognition in presence of an animal pet using thermal sensor array. Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, Corfu, Greece."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.neunet.2019.11.002","article-title":"Local distinguishability aggrandizing network for human anomaly detection","volume":"122","author":"Gong","year":"2020","journal-title":"Neural Netw."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.ins.2020.03.021","article-title":"PUMAD: PU Metric Learning for Anomaly Detection","volume":"523","author":"Ju","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.ins.2016.08.006","article-title":"Online personal risk detection based on behavioural and physiological patterns","volume":"384","author":"Trejo","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Borazio, M., Berlin, E., K\u00fcc\u00fckyildiz, N., Scholl, P., and Van Laerhoven, K. (2014, January 15\u201317). Towards benchmarked sleep detection with wrist-worn sensing units. Proceedings of the 2014 IEEE International Conference on Healthcare Informatics, Verona, Italy.","DOI":"10.1109\/ICHI.2014.24"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hu, R., Pham, H., Buluschek, P., and Gatica-Perez, D. (2017, January 23). Elderly People Living Alone: Detecting Home Visits with Ambient and Wearable Sensing. Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care, Mountain View, CA, USA.","DOI":"10.1145\/3132635.3132649"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1515\/slgr-2015-0039","article-title":"Entropy-based algorithms in the analysis of biomedical signals","volume":"43","author":"Borowska","year":"2015","journal-title":"Stud. Logic Gramm. Rhetor."},{"key":"ref_17","unstructured":"Howedi, A., Lotfi, A., and Pourabdollah, A. (July, January 30). A multi-scale fuzzy entropy measure for anomaly detection in activities of daily living. Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, Corfu, Greece."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.ins.2020.03.034","article-title":"SiTGRU: Single-Tunnelled Gated Recurrent Unit for Abnormality Detection","volume":"524","author":"Fanta","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_20","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 Recognit."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.ins.2017.05.021","article-title":"Anomaly detection based on a dynamic Markov model","volume":"411","author":"Ren","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.procs.2017.06.121","article-title":"Activity recognition and abnormal behaviour detection with recurrent neural networks","volume":"110","author":"Arifoglu","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Poh, S.C., Tan, Y.F., Guo, X., Cheong, S.N., Ooi, C.P., and Tan, W.H. (2019, January 15\u201317). LSTM and HMM comparison for home activity anomaly detection. Proceedings of the 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China.","DOI":"10.1109\/ITNEC.2019.8729168"},{"key":"ref_25","first-page":"1","article-title":"Learning setting-generalized activity models for smart spaces","volume":"2010","author":"Cook","year":"2010","journal-title":"IEEE Intell. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.infsof.2017.07.009","article-title":"An anomaly detection system based on variable N-gram features and one-class SVM","volume":"91","author":"Khreich","year":"2017","journal-title":"Inf. Softw. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.patcog.2018.05.002","article-title":"Improving one class support vector machine novelty detection scheme using nonlinear features","volume":"83","author":"Sadooghi","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_28","unstructured":"Ma, J., and Perkins, S. (2003, January 20\u201324). Time-series novelty detection using one-class support vector machines. Proceedings of the International Joint Conference on Neural Networks, Portland, OR, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hoque, E., Dickerson, R.F., Preum, S.M., Hanson, M., Barth, A., and Stankovic, J.A. (2015, January 10\u201312). Holmes: A comprehensive anomaly detection system for daily in-home activities. Proceedings of the 2015 International Conference on Distributed Computing in Sensor Systems, Fortaleza, Brazil.","DOI":"10.1109\/DCOSS.2015.20"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.procs.2020.03.082","article-title":"A New Device to Track and Identify people in a Multi-Residents Context","volume":"170","author":"Lapointe","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.future.2020.04.039","article-title":"Multi-resident type recognition based on ambient sensors activity","volume":"112","author":"Li","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, T., and Cook, D.J. (2020). sMRT: Multi-Resident Tracking in Smart Homes with Sensor Vectorization. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2020.2973571"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yuan, W., Cao, J., Jin, Z., Xia, F., and Wang, R. (2019, January 22\u201325). A Multi-resident Activity Recognition Approach based on Frequent Itemset Mining Features. Proceedings of the 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), Helsinki, Finland.","DOI":"10.1109\/INDIN41052.2019.8972016"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Aicha, A.N., Englebienne, G., and Kr\u00f6se, B. (2014, January 13). Modeling visit behaviour in smart homes using unsupervised learning. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, Seattle, WA, USA.","DOI":"10.1145\/2638728.2638809"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","unstructured":"Aicha, A.N., Englebienne, G., and Kr\u00f6se, B. (2012, January 6\u20139). How busy is my supervisor?: Detecting the visits in the office of my supervisor using a sensor network. Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments, Heraklion, Crete, Greece.","DOI":"10.1145\/2413097.2413112"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Nait Aicha, A., Englebienne, G., and Kr\u00f6se, B. (2013, January 8). How lonely is your grandma?: Detecting the visits to assisted living elderly from wireless sensor network data. Proceedings of the ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, Zurich, Switzerland.","DOI":"10.1145\/2494091.2497283"},{"key":"ref_38","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 Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, San Diego, CA, USA.","DOI":"10.1109\/EMBC.2012.6347324"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Nguyen, D., Nguyen, L., and Nguyen, S. (2020). A Novel Approach of Ontology-Based Activity Segmentation and Recognition Using Pattern Discovery in Multi-resident Homes. Frontiers in Intelligent Computing: Theory and Applications, Springer.","DOI":"10.1007\/978-981-32-9186-7_19"},{"key":"ref_40","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_41","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_42","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_43","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_44","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_45","doi-asserted-by":"crossref","unstructured":"Emi, I.A., and Stankovic, J.A. (2015, January 14). SARRIMA: Smart ADL recognizer and resident identifier in multi-resident accommodations. Proceedings of the Conference on Wireless Health, bethesda, MA, USA.","DOI":"10.1145\/2811780.2811916"},{"key":"ref_46","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."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Howedi, A., Lotfi, A., and Pourabdollah, A. (2019). Exploring Entropy Measurements to Identify Multi-Occupancy in Activities of Daily Living. Entropy, 21.","DOI":"10.3390\/e21040416"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"51","DOI":"10.3233\/AIS-2009-0007","article-title":"Multi-agent smart environments","volume":"1","author":"Cook","year":"2009","journal-title":"J. Ambient. Intell. Smart Environ."},{"key":"ref_49","unstructured":"R\u00e9nyi, A. On measures of entropy and information. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/LSP.2016.2542881","article-title":"Dispersion entropy: A measure for time-series analysis","volume":"23","author":"Rostaghi","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_52","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_53","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_54","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_55","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_56","doi-asserted-by":"crossref","first-page":"174102","DOI":"10.1103\/PhysRevLett.88.174102","article-title":"Permutation entropy: A natural complexity measure for time series","volume":"88","author":"Bandt","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1007\/s11517-017-1647-5","article-title":"Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis","volume":"55","author":"Azami","year":"2017","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"021906","DOI":"10.1103\/PhysRevE.71.021906","article-title":"Multiscale entropy analysis of biological signals","volume":"71","author":"Costa","year":"2005","journal-title":"Phys. Rev. E"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Aziz, W., and Arif, M. (2005, January 24\u201325). Multiscale permutation entropy of physiological time series. Proceedings of the 2005 Pakistan Section Multitopic Conference, Karachi, Pakistan.","DOI":"10.1109\/INMIC.2005.334494"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3123","DOI":"10.1177\/1077546314520830","article-title":"A multiscale permutation entropy based approach to select wavelet for fault diagnosis of ball bearings","volume":"21","author":"Vakharia","year":"2015","journal-title":"J. Vib. Control."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Howedi, A., Lotfi, A., and Pourabdollah, A. (2019, January 5\u20137). Distinguishing activities of daily living in a multi-occupancy environment. Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments, Island of Rhodes, Greece.","DOI":"10.1145\/3316782.3322782"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.mechmachtheory.2014.03.014","article-title":"A rolling bearing fault diagnosis method based on multi-scale fuzzy entropy and variable predictive model-based class discrimination","volume":"78","author":"Zheng","year":"2014","journal-title":"Mech. Mach. Theory"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1007\/s10921-019-0623-4","article-title":"A Multi-scale Fuzzy Measure Entropy and Infinite Feature Selection Based Approach for Rolling Bearing Fault Diagnosis","volume":"38","author":"Zhu","year":"2019","journal-title":"J. Nondestruct. Eval."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/8\/845\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:53:09Z","timestamp":1760176389000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/8\/845"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,30]]},"references-count":63,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["e22080845"],"URL":"https:\/\/doi.org\/10.3390\/e22080845","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,30]]}}}