论文标题
智能手机，智能手表和可穿戴加速度计的“最适合”步行识别方法
A 'one-size-fits-most' walking recognition method for smartphones, smartwatches, and wearable accelerometers
论文作者
论文摘要
个人数字设备的无处不在提供了研究人类行为的前所未有的机会。当前的最新方法使用“活动计数”量化了体育活动，该度量忽略了特定类型的身体活动。我们提出了用于亚秒三轴加速度计数据的步行识别方法，其中活动分类基于步行的固有特征：强度，周期性和持续时间。我们针对20个公开可用的数据集验证了我们的方法，该方法在各个身体位置（大腿，腰，胸部，手臂，手腕）收集的有关步行活动数据的注释数据集。我们证明了我们的方法可以以高灵敏度和特异性估算步行期：在各个身体位置，平均灵敏度在0.92至0.97之间，常见日常活动的平均特异性通常高于0.95。我们还评估了该方法对人口统计学和人体测量变量和测量环境（身体位置，环境）的算法公平性。最后，我们在Matlab和Python中发布了作为开源软件的方法。
The ubiquity of personal digital devices offers unprecedented opportunities to study human behavior. Current state-of-the-art methods quantify physical activity using 'activity counts,' a measure which overlooks specific types of physical activities. We proposed a walking recognition method for sub-second tri-axial accelerometer data, in which activity classification is based on the inherent features of walking: intensity, periodicity, and duration. We validated our method against 20 publicly available, annotated datasets on walking activity data collected at various body locations (thigh, waist, chest, arm, wrist). We demonstrated that our method can estimate walking periods with high sensitivity and specificity: average sensitivity ranged between 0.92 and 0.97 across various body locations, and average specificity for common daily activities was typically above 0.95. We also assessed the method's algorithmic fairness to demographic and anthropometric variables and measurement contexts (body location, environment). Finally, we have released our method as open-source software in MATLAB and Python.
