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

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 publicationProceedings of IEEE International Conference on Ubiquitous Intelligence and Computing (UIC)
PublisherIEEE
Pages(In-press)
ISBN (Print)(In-press)
Publication statusAccepted/In press - 30 Apr 2019
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

Conference

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

Fingerprint

Distribution functions
Coffee
Medical problems
Learning algorithms
Learning systems
Sensors
Tea

Keywords

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

Cite this

Konios, A., Garcia-Constantino, M., Christopoulos, S., Mustafa, M., Ekerete, I., Shewell, C., ... Morrison, G. (Accepted/In press). Probabilistic Analysis of Temporal and Sequential Aspects of Activities of Daily Living for Abnormal Behaviour Detection. In Proceedings of IEEE International Conference on Ubiquitous Intelligence and Computing (UIC) (pp. (In-press)). IEEE.

Probabilistic Analysis of Temporal and Sequential Aspects of Activities of Daily Living for Abnormal Behaviour Detection. / Konios, Alexandros; Garcia-Constantino, Matias; Christopoulos, Stavros; Mustafa, Mustafa; Ekerete, Idongesit; Shewell, Colin; Nugent, Chris; Morrison, Gareth.

Proceedings of IEEE International Conference on Ubiquitous Intelligence and Computing (UIC). IEEE, 2019. p. (In-press).

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

Konios, A, Garcia-Constantino, M, Christopoulos, S, Mustafa, M, Ekerete, I, Shewell, C, Nugent, C & Morrison, G 2019, Probabilistic Analysis of Temporal and Sequential Aspects of Activities of Daily Living for Abnormal Behaviour Detection. in Proceedings of IEEE International Conference on Ubiquitous Intelligence and Computing (UIC). IEEE, pp. (In-press), IEEE International Conference on Ubiquitous Intelligence and Computing , Leicester, United Kingdom, 19/08/19.
Konios A, Garcia-Constantino M, Christopoulos S, Mustafa M, Ekerete I, Shewell C et al. Probabilistic Analysis of Temporal and Sequential Aspects of Activities of Daily Living for Abnormal Behaviour Detection. In Proceedings of IEEE International Conference on Ubiquitous Intelligence and Computing (UIC). IEEE. 2019. p. (In-press)
Konios, Alexandros ; Garcia-Constantino, Matias ; Christopoulos, Stavros ; Mustafa, Mustafa ; Ekerete, Idongesit ; Shewell, Colin ; Nugent, Chris ; Morrison, Gareth. / Probabilistic Analysis of Temporal and Sequential Aspects of Activities of Daily Living for Abnormal Behaviour Detection. Proceedings of IEEE International Conference on Ubiquitous Intelligence and Computing (UIC). IEEE, 2019. pp. (In-press)
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N2 - 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.

AB - 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.

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