Mohan, Prakhar (2020-04). Longitudinal Assessment of Daily Routine Uniformity in a Smart Home Environment Using Hierarchical Clustering. Master's Thesis.
Thesis
The gradual decline in routine patterns is a major symptom of early-stage dementia, therefore an unobtrusive real-life assessment of the elder's routine can potentially be of significant clinical importance. This research focuses on the assessment of changes in a person's daily routine using longitudinal data recorded from a network of non-intrusive motion sensors in a smart home environment. We propose to identify repeating patterns in a person's daily routine over the span of multiple days using hierarchical clustering algorithms, which allow us to disregard noisy signal patterns and various confounding factors that contribute to the momentary variability of the sensor data. We have evaluated our proposed algorithm on both synthetic and real-world data recorded in the span of 50-100 days from four elderly adults. Our results indicate that the proposed hierarchical clustering approach can more reliably quantify the degree of routinness compared to baseline approaches that compare the routines of two consecutive days or capture variations in the occurrence of recognised activities.
