深度CHORES：使用深度学习估计身体活动的标志性指标
Deep CHORES: Estimating Hallmark Measures of Physical Activity Using Deep Learning
摘要：
随着智能手表技术的出现，用于评估身体活动（PA）标志性指标的腕部加速度计正在迅速发展。鉴于腕戴式加速度计的日益普及，需要对识别（PA）类型和估计整个寿命期间的能量消耗（EE）进行严格的评估。参与者（66%女性，年龄20-89岁）在标准化实验室环境中进行了33项日常活动，同时三轴加速度计从右手腕收集数据。佩戴便携式代谢装置以测量代谢强度。我们构建了深度学习网络，从时间序列数据中提取空间和时间表示，并使用它们来识别PA类型和估计EE。深度学习模型具有很高的性能; F1得分分别为：0.82，0.81和95，用于识别久坐，运动和生活方式活动。EE估计值的均方根误差为1.1（+/-0.13）。
英文摘要：
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.
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