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Probabilistic Analysis of Abnormal Behaviour Detection in Activities of Daily Living

Garcia-Constantino, M., KONIOS, Alexandros, Ekerete, I., Christopoulos, S.-R. G., Shewell, C., Nugent, C. and Morrison, G. (2019) Probabilistic Analysis of Abnormal Behaviour Detection in Activities of Daily Living. In: 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, pp. 461-466. ISBN 978-1-5386-9151-9

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Abstract or description

This paper presents a probabilistic approach for the identification of abnormal behaviour in Activities of Daily Living (ADLs) from sensor data collected from 30 participants. The ADLs considered are: (i) preparing and drinking tea, and (ii) preparing and drinking coffee. Abnormal behaviour identified in the context of these activities can be an indicator of a progressive health problem or the occurrence of a hazardous incident. The approach presented considers the temporal aspect of the sequences of actions that are part of each ADL and that vary between participants. The average and standard deviation for the durations of each action were calculated to define an average time and a range in which a behaviour could be considered as normal for each stage and activity. The Cumulative Distribution Function (CDF) was used to obtain the probabilities of abnormal behaviours related to the early and late completion of activities and stages within an activity. The data analysis show that CDF can provide accurate and reliable results regarding the presence of abnormal behaviour in stages and activities that last over a minute. Finally, this approach could be used to train machine learning algorithms for the abnormal behaviour detection.

Item Type: Book Chapter, Section or Conference Proceeding
Additional Information: “© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Faculty: School of Computing and Digital Technologies > Computing
Event Title: 2019 IEEE International Conference on Pervasive Computing and Communications
Depositing User: Alexandros KONIOS
Date Deposited: 16 Dec 2020 10:48
Last Modified: 17 Dec 2020 04:30
URI: http://eprints.staffs.ac.uk/id/eprint/6674

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