Paper ID: 2207.01809

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

Yiren Wang, Fatima Tuz-Zahra, Rong Zablocki, Chongzhi Di, Marta M. Jankowska, John Bellettiere, Jordan A. Carlson, Andrea Z. LaCroix, Sheri J. Hartman, Dori E. Rosenberg, Jingjing Zou, Loki Natarajan

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.

Submitted: Jul 5, 2022