Discovering Behavioral Predispositions in Data to Improve Human Activity Recognition

TL;DR

This paper improves human activity recognition in dementia patients by identifying behavioral predispositions through clustering annotation distributions, enabling more accurate predictions of behaviors like apathy and agitation.

Contribution

It introduces a novel method to detect behavioral predispositions using clustering of annotation data, enhancing activity recognition accuracy.

Findings

Recognition performance improves with BPD knowledge

Clustering reveals meaningful behavioral segments

Method addresses variability in patient behaviors

Abstract

The automatic, sensor-based assessment of challenging behavior of persons with dementia is an important task to support the selection of interventions. However, predicting behaviors like apathy and agitation is challenging due to the large inter- and intra-patient variability. Goal of this paper is to improve the recognition performance by making use of the observation that patients tend to show specific behaviors at certain times of the day or week. We propose to identify such segments of similar behavior via clustering the distributions of annotations of the time segments. All time segments within a cluster then consist of similar behaviors and thus indicate a behavioral predisposition (BPD). We utilize BPDs by training a classifier for each BPD. Empirically, we demonstrate that when the BPD per time segment is known, activity recognition performance can be substantially improved.