Karas Marta, Bai Jiawei, Strączkiewicz Marcin, Harezlak Jaroslaw, Glynn Nancy W, Harris Tamara, Zipunnikov Vadim, Crainiceanu Ciprian, Urbanek Jacek K
Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Tel.: +1-317-665-4551,
Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University.
Stat Biosci. 2019 Jul;11(2):210-237. doi: 10.1007/s12561-018-9227-2. Epub 2019 Jan 12.
Wearable accelerometers provide detailed, objective, and continuous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popularity of wearable technology in health research. An ever-increasing number of studies collect high-throughput, sub-second level raw acceleration data. In this paper, we discuss problems related to the collection and analysis of raw accelerometry data and refer to published solutions. In particular, we describe the size and complexity of the data, the within- and between-subject variability, and the effects of sensor location on the body. We also discuss challenges related to sampling frequency, device calibration, data labeling and multiple PA monitors synchronization. We illustrate these points using the Developmental Epidemiological Cohort Study (DECOS), which collected raw accelerometry data on individuals both in a controlled and the free-living environment.
可穿戴式加速度计可提供详细、客观且连续的身体活动（PA）测量数据。技术的最新进展以及可穿戴设备成本的降低，使得可穿戴技术在健康研究中的普及程度呈爆发式增长。越来越多的研究收集高通量、亚秒级别的原始加速度数据。在本文中，我们讨论了与原始加速度计数据的收集和分析相关的问题，并参考了已发表的解决方案。特别是，我们描述了数据的规模和复杂性、个体内部和个体之间的变异性，以及传感器在身体上的位置所产生的影响。我们还讨论了与采样频率、设备校准、数据标注以及多个PA监测器同步相关的挑战。我们通过发育流行病学队列研究（DECOS）来说明这些要点，该研究在受控环境和自由生活环境中收集了个体的原始加速度计数据。
