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

arXiv: https://arxiv.org/abs/2207.01809 · HTML: https://mlsanity.com/paper/2207.01809/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 1 of 1)

- [ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data](https://mlsanity.com/paper/1309.3295/) (2013)

## Abstract (arXiv)

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.