Sedentary Behavior Estimation with Hip-worn Accelerometer Data: Segmentation, Classification and Thresholding
This addresses the need for more accurate and robust methods in cohort studies to monitor sedentary behavior, which is incremental over previous approaches.
The paper tackled the problem of estimating sedentary behavior from hip-worn accelerometer data under free-living conditions, proposing a local Markov switching model with changepoint detection and two-stage classification that achieved over 80% accuracy.
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.