Objective: Commercial and research-grade wearable devices have become increasingly popular over the past decade. Information extracted from devices using accelerometers is frequently summarized as ``number of steps" (commercial devices) or ``activity counts" (research-grade devices). Raw accelerometry data that can be easily extracted from accelerometers used in research, for instance ActiGraph GT3X+, are frequently discarded. Approach: Our primary goal is proposing an innovative use of the {\em de-shape synchrosqueezing transform} to analyze the raw accelerometry data recorded from a single sensor installed in different body locations, particularly the wrist, to extract {\em gait cadence} when a subject is walking. The proposed methodology is tested on data collected in a semi-controlled experiment with 32 participants walking on a one-kilometer predefined course. Walking was executed on a flat surface as well as on the stairs (up and down). Main Results: The cadences of walking on a flat surface, ascending stairs, and descending stairs, determined from the wrist sensor, are 1.98$\pm$0.15 Hz, 1.99$\pm$0.26 Hz, and 2.03$\pm$0.26 Hz respectively. The cadences are 1.98$\pm$0.14 Hz, 1.97$\pm$0.25 Hz, and 2.02$\pm$0.23 Hz, respectively if determined from the hip sensor, 1.98$\pm$0.14 Hz, 1.93$\pm$0.22 Hz and 2.06$\pm$0.24 Hz, respectively if determined from the left ankle sensor, and 1.98$\pm$0.14 Hz, 1.97$\pm$0.22 Hz, and 2.04$\pm$0.24 Hz, respectively if determined from the right ankle sensor. The difference is statistically significant indicating that the cadence is fastest while descending stairs and slowest when ascending stairs. Also, the standard deviation when the sensor is on the wrist is larger. These findings are in line with our expectations. Conclusion: We show that our proposed algorithm can extract the cadence with high accuracy, even when the sensor is placed on the wrist.

翻译：目标：过去十年中，商用和研究级可穿戴设备日益普及。使用加速度计的设备所提取的信息通常被总结为“步数”（商用设备）或“活动计数”（研究级设备）。研究中使用且易于从加速度计（例如ActiGraph GT3X+）提取的原始加速度数据常被丢弃。方法：我们的主要目标是创新性地使用去形状同步压缩变换分析安装在身体不同位置（特别是手腕）的单传感器记录的原始加速度数据，从而在受试者行走时提取步频。该提出的方法在半受控实验中进行了测试，实验包括32名参与者沿预定的一公里路线行走，行走在平坦地面以及楼梯（上下）上进行。主要结果：从手腕传感器确定的平地行走、上楼梯和下楼梯的步频分别为1.98±0.15 Hz、1.99±0.26 Hz和2.03±0.26 Hz。从髋部传感器确定的相应步频依次为1.98±0.14 Hz、1.97±0.25 Hz和2.02±0.23 Hz；从左踝传感器确定的依次为1.98±0.14 Hz、1.93±0.22 Hz和2.06±0.24 Hz；从右踝传感器确定的依次为1.98±0.14 Hz、1.97±0.22 Hz和2.04±0.24 Hz。组间差异具有统计学显著性，表明下楼梯时步频最快，上楼梯时最慢。此外，传感器位于手腕时标准差更大。这些发现符合我们的预期。结论：我们证明了所提出的算法即使传感器置于手腕也能高精度地提取步频。