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arXiv · 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
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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.

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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. 2022-07-05. Sedentary Behavior Estimation with Hip-worn Accelerometer Data: Segmentation, Classification and Thresholding. https://arxiv.org/abs/2207.01809

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