Probabilistic Analysis of Temporal and Sequential Aspects of Activities of Daily Living for Abnormal Behaviour Detection

Alexandros Konios, Matias Garcia-Constantino, Stavros Christopoulos, Mustafa Mustafa, Idongesit Ekerete, Colin Shewell, Chris Nugent, Gareth Morrison

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

1 Citation (Scopus)
2 Downloads (Pure)

Abstract

This paper presents a probabilistic approach for the identification of abnormal behaviour in Activities of Daily Living (ADLs) from dense sensor data collected from 30 participants. The ADLs considered are related to preparing and drinking (i) tea, and (ii) 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 and sequential aspects of the actions that are part of each ADL and that vary between participants.The average and standard deviation for the duration and number of steps of each activity are calculated to define the average time and steps and a range within which a behaviour could be considered as normal for each stage and activity. The Cumulative Distribution Function (CDF) is used to obtain the probabilities of abnormal behaviours related to the early and late completion of activities and stages within an activity in terms of time and steps. Analysis shows that CDF can provide precise and reliable results regarding the presence of abnormal behaviour in stages and activities that last over a minute or consist of many steps. Finally, this approach could be used to train machine learning algorithms for abnormal behaviour detection.
Original languageEnglish
Title of host publication2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
PublisherIEEE
Pages723-730
Number of pages8
ISBN (Electronic)978-1-7281-4034-6
DOIs
Publication statusPublished - 9 Apr 2020
EventIEEE International Conference on Ubiquitous Intelligence and Computing - Leicester, United Kingdom
Duration: 19 Aug 201923 Aug 2019
Conference number: 16
http://www.smart-world.org/2019/uic/cfp_d.php

Publication series

NameProceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019

Conference

ConferenceIEEE International Conference on Ubiquitous Intelligence and Computing
Abbreviated titleUIC 2019
CountryUnited Kingdom
CityLeicester
Period19/08/1923/08/19
Internet address

Bibliographical note

© 2020 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.

Keywords

  • Activities of Daily Living, ADLs
  • Cumulative Distribution Function, CDF
  • Probabilistic Analysis

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    Konios, A., Garcia-Constantino, M., Christopoulos, S., Mustafa, M., Ekerete, I., Shewell, C., ... Morrison, G. (2020). Probabilistic Analysis of Temporal and Sequential Aspects of Activities of Daily Living for Abnormal Behaviour Detection. In 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 723-730). [9060148] (Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019). IEEE. https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00158