Clustering of free-living physical activity patterns and association in mental health studies

Oregon Convention Center 

Monitoring free-living physical activity (PA) can provide valuable insight into daily life activities. However, variations of PA, including the within-subject and between-subject variation, are often large and cause difficulty in analysis. In addition, due to its longitudinal characteristic, it is challenging to summarize and extract interpretable features. In this paper, we propose for such function data an elastic-based clustering algorithm for detecting specific changes in activity patterns. The process of this algorithm includes segmentation, data similarity computation, and pattern clustering. Using this clustering algorithm, we can obtain subject-specific and cluster-specific activity mean functions and perform association analysis to explore the relationship between physical activity and health outcome of interest. This algorithm can detect the phase and amplitude variation, reduce data dimension and offer interpretable findings. The proposed method is demonstrated on mental health studies. The results provide cluster-specific patterns for physical activities and can describe the patterns that are associated with the phy

wearable device

functional data analysis

clustering

free-living physical activity 

Biometrics Section