基于髋关节加速度传感器数据的久坐行为估计：分割、分类和保持
Sedentary Behavior Estimation with Hip-worn Accelerometer Data: Segmentation, Classification and Thresholding
摘要：
队列研究越来越多地使用加速度计进行身体活动和久坐行为估计。这些设备往往比自我报告更不容易出错，可以捕获全天的活动，并且经济实惠。然而，以前的方法估计久坐行为的基础上臀部磨损的数据往往是无效的或次优的自由生活的情况下，受试者到受试者的变化。在本文中，我们提出了一个本地马尔可夫切换模型，考虑到这种情况，并介绍了一个一般程序的姿势分类和久坐不动的行为分析，自然适合该模型。我们的方法具有时间序列中的变点检测方法，以及将数据标记为3类（坐，站，走）的两阶段分类步骤。通过严格的训练测试范例，我们表明我们的方法达到了> 80%的准确率。此外，我们的方法是强大的，易于解释。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
