Ambient and Wearable Sensor Fusion for Abnormal Behaviour Detection in Activities of Daily Living


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.

In Proceedings - PervasiveHealth - Pervasive Computing Technologies for Healthcare. 18th IEEE International Conference on Pervasive Computing and Communications (PerCom2020)
Matias Garcia-Constantino
Lecturer in Computer Science

My research interests include Data Analysis, Internet of Things (IoT), Artificial Intelligence, Human-Computer Interaction and Network Science. matter.