论文标题
深度琐事：使用深度学习估算体育活动的标志
Deep CHORES: Estimating Hallmark Measures of Physical Activity Using Deep Learning
论文作者
论文摘要
随着智能手表技术的出现，用于评估体育锻炼标志（PA）的标志性测量值的手腕加速度计正在迅速增长。鉴于腕上加速度计的普及度越来越高，需要进行严格的评估以识别（PA）类型和整个寿命的能量消耗（EE）。参与者（66％的女性，20 - 89年）在标准化的实验室环境中进行了33次每日活动，而三轴加速度计收集了右手腕的数据。佩戴便携式代谢单位以测量代谢强度。我们构建了深度学习网络，以从时间序列数据中提取空间和时间表示，并用它们来识别PA类型并估计EE。深度学习模型导致了高性能。 F1得分分别为久坐，运动和生活方式活动，为0.82、0.81和95。对于EE的估计，均方根误差为1.1（+/- 0.13）。
Wrist accelerometers for assessing hallmark measures of physical activity (PA) are rapidly growing with the advent of smartwatch technology. Given the growing popularity of wrist-worn accelerometers, there needs to be a rigorous evaluation for recognizing (PA) type and estimating energy expenditure (EE) across the lifespan. Participants (66% women, aged 20-89 yrs) performed a battery of 33 daily activities in a standardized laboratory setting while a tri-axial accelerometer collected data from the right wrist. A portable metabolic unit was worn to measure metabolic intensity. We built deep learning networks to extract spatial and temporal representations from the time-series data, and used them to recognize PA type and estimate EE. The deep learning models resulted in high performance; the F1 score was: 0.82, 0.81, and 95 for recognizing sedentary, locomotor, and lifestyle activities, respectively. The root mean square error was 1.1 (+/-0.13) for the estimation of EE.
