Analysis of Accelerometer Data for Personalised Abnormal Behaviour Detection in Activities of Daily Living

Matias Garcia-Constantino, Alexandros Konios, Irvin Hussein Lopez-Nava, Pierre Pouliet, Idongesit F. Ekerete, Mustafa A. Mustafa, Chris Nugent, Gareth Morrison

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This paper proposes a novel approach to identify personalised abnormal behaviour in Activities of Daily Living (ADLs) using accelerometer sensor data. 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. Monitoring ADLs for detecting abnormal behaviour is of particular importance due to the potential life changing consequences that could result from not acting timely. Prior to performing ADLs, the participants were asked six questions related to their well-being and mood. In addition to data collected from accelerometers, data was also collected from contact and thermal sensors, and radar. The work presented is a first step towards a more personalised approach in which individual user profiles are considered as it is acknowledged that people behave differently from each other. Thus, data was collected seven times for each participant. We have evaluated our approach with accelerometer data collected from 15 participants. The experimental results show that accelerometer data is sufficient to identify the main stages of the ADLs considered, and therefore, any unusual changes in the signals and duration could mean that abnormal behaviour occurred.

Keywords: Activities of Daily Living · ADLs · Activity Recognition · Accelerometer · Mood · Sensors.
Original languageEnglish
Title of host publication14th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022)
Publication statusAccepted/In press - 2 Sept 2022


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