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.
翻译:科霍特研究正在越来越多地使用加速度计进行体育活动和定时行为估计。 这些装置往往比自我报告容易出错,能够全天捕捉活动,而且经济。 但是,以前基于时表数据估计定时行为的方法往往无效或低于最优程度,在自由生活状态下和主题变量下。 在本文中,我们提出了一个考虑到这种情况的本地Markov切换模型,并引入符合模型自然的姿态分类和定时行为分析一般程序。 我们的方法特征是改变时间序列中的定点探测方法,以及将数据标记为3级的两阶段分类步骤。通过严格的培训测试模式,我们显示我们的方法达到了80%的精确度。此外,我们的方法既健全又容易解释。