Department of Neurology, Kiel University, Arnold-Heller-Straße 3, 24105 Kiel, Germany.
Faculty of Engineering, Kiel University, Kaiserstraße 2, 24143 Kiel, Germany.
Sensors (Basel). 2020 Oct 21;20(20):5963. doi: 10.3390/s20205963.
Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson's disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from -0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson's disease.
神经病理学可能会改变行走时手臂的摆动运动。因此，手臂摆动的量化具有很高的临床意义。本研究为健康成年人和帕金森病患者（PwP）开发并验证了一种基于可穿戴传感器的手臂摆动算法。使用可穿戴传感器在跑步机上行走时评估了 15 名健康成年人和 13 名 PwP 的手臂摆动情况（i），以及使用固定在各自传感器顶部的反光标记进行光学运动捕捉以进行验证（ii）。可穿戴传感器的陀螺仪数据用于计算几个手臂摆动参数，包括幅度和峰值角速度。手臂摆动幅度和峰值角速度的提取具有系统误差，范围分别为 0.1 到 0.5°和 -0.3 到 0.3°/s。根据文献，这些提取的参数在健康成年人和 PwP 之间存在显著差异。已经开发出一种准确的算法，可用于临床和日常生活情况。该算法为在健康成年人和患有运动障碍（如帕金森病）的患者中使用可穿戴传感器提取的手臂摆动参数提供了基础。
