Authors:
Jiawei Zheng
and
Petros Papapanagiotou
Affiliation:
School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh, EH8 9AB, U.K.
Keyword(s):
Activities of Daily Living, Behaviour Monitoring, Deviation Detection, Predictive Modelling, Sensor Data.
Abstract:
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.