论文标题
脱形同步器的应用以估计位于不同人体位置的单发音计的步态节奏
Application of de-shape synchrosqueezing to estimate gait cadence from a single-sensor accelerometer placed in different body locations
论文作者
论文摘要
目的：商业和研究级可穿戴设备在过去十年中越来越受欢迎。使用加速度计从设备中提取的信息经常总结为``步骤数'（商业设备）或``活动级数'（研究级设备）。经常丢弃从研究中使用的加速度计（例如Actigraph GT3X+）从加速度计中提取的原始加速度计数据。方法：我们的主要目标是提出{\ em de-em de-de-de-em de-synchrosQueezing变换}的创新使用，以分析从安装在不同身体位置的单个传感器（尤其是腕部）中的单个传感器记录的原始加速度计数据，以提取{\ em步态性cadence}时，当受试者步行时。对在半控件实验中收集的数据进行了测试，其中32名参与者在一个公里的预定义过程中行走。步行是在平坦的表面和楼梯上（上下）执行的。主要结果：根据手腕传感器确定的平坦表面，上升的楼梯和下降楼梯的节奏为1.98 $ \ pm $ 0.15 Hz，1.99 $ \ pm $ 0.26 Hz和2.03 $ \ pm $ 0.26 Hz。 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和2.04 $ \ pm $ 0.24 Hz，如果从正确的脚踝传感器确定。区别在统计上是显着的，表明节奏在下楼梯时最快，并且在上升楼梯时最慢。同样，当传感器在手腕上时，标准偏差更大。这些发现符合我们的期望。结论：我们表明，即使将传感器放在手腕上，我们提出的算法也可以高精度提取节奏。
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.
