Motif clustering and digital biomarker extraction for free-living physical activity analysis.

ABSTRACT:

Background

Analyzing free-living physical activity (PA) data presents challenges due to variability in daily routines and the lack of activity labels. Traditional approaches often rely on summary statistics, which may not capture the nuances of individual activity patterns. To address these limitations and advance our understanding of the relationship between PA patterns and health outcomes, we propose a novel motif clustering algorithm that identifies and characterizes specific PA patterns.

Methods

This paper proposes an elastic distance-based motif clustering algorithm for identifying specific PA patterns (motifs) in free-living PA data. The algorithm segments long-term PA curves into short-term segments and utilizes elastic shape analysis to measure the similarity between

SUBMITTER: Liang YT 

PROVIDER: S-EPMC11753168 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature