Sedentary Behavior Estimation with Hip-worn Accelerometer Data: Segmentation, Classification and Thresholding

TL;DR

This paper introduces a local Markov switching model for accurately classifying sedentary behavior using hip-worn accelerometer data, addressing variability in free-living conditions with high accuracy and interpretability.

Contribution

It presents a novel segmentation and classification method that improves sedentary behavior estimation from hip accelerometers under real-world conditions.

Findings

Achieves over 80% classification accuracy.

Robust and easy-to-interpret methodology.

Effective in free-living, subject-to-subject scenarios.

Abstract

Cohort studies are increasingly using accelerometers for physical activity and sedentary behavior estimation. These devices tend to be less error-prone than self-report, can capture activity throughout the day, and are economical. However, previous methods for estimating sedentary behavior based on hip-worn data are often invalid or suboptimal under free-living situations and subject-to-subject variation. In this paper, we propose a local Markov switching model that takes this situation into account, and introduce a general procedure for posture classification and sedentary behavior analysis that fits the model naturally. Our method features changepoint detection methods in time series and also a two stage classification step that labels data into 3 classes(sitting, standing, stepping). Through a rigorous training-testing paradigm, we showed that our approach achieves > 80% accuracy. In addition, our method is robust and easy to interpret.