Predictive Behavioural Monitoring and Deviation Detection in Activities of Daily Living of Older Adults

Jiawei Zheng, Petros Papapanagiotou

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

Abstract / Description of output

Predictive behaviour monitoring of Activities of Daily Living (ADLs) can provide unique, personalised insights about an older person’s physical and cognitive health and lead to unique opportunities to support self- management, proactive intervention and promote independent living. In this paper, we analyse ADL data from ambient sensors to model behaviour markers on a daily basis. Using a number of machine learning and statistical methods we model a predicted daily routine for each marker, detect deviations based on a set of relative thresholds and calculate long-term drifts. We further analyse the causal factors of deviations by investigating relationships between different activities. We demonstrate our results using data from a sample of 11 participants from the CASAS dataset. Finally, we develop a dashboard to visualize our computed daily routines and quantified deviations in an attempt to offer useful feedback to the monitored person and their caregivers.
Original languageEnglish
Title of host publication15th International Joint Conference on Biomedical Engineering Systems and Technologies, Volume 5 - HEALTHINF
EditorsNathalie Bier, Ana Fred, Hugo Gamboa
Number of pages12
ISBN (Electronic)978-989-758-552-4
Publication statusPublished - 24 Feb 2022
Event15th International Conference on Health Informatics 2022 - Virtual Conference
Duration: 9 Feb 202211 Feb 2022
Conference number: 15

Publication series

ISSN (Electronic)2184-4305


Conference15th International Conference on Health Informatics 2022
Abbreviated titleHEALTHINF 2022
Internet address

Keywords / Materials (for Non-textual outputs)

  • Activities of Daily Living
  • Behaviour Monitoring
  • Deviation Detection
  • Predictive Modelling
  • Sensor Data


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