IEEE Trans Neural Syst Rehabil Eng. 2024;32:2239-2249. doi: 10.1109/TNSRE.2024.3407887. Epub 2024 Jun 24.
In Huntington's disease (HD), wearable inertial sensors could capture subtle changes in motor function. However, disease-specific validation of methods is necessary. This study presents an algorithm for walking bout and gait event detection in HD using a leg-worn accelerometer, validated only in the clinic and deployed in free-living conditions. Seventeen HD participants wore shank- and thigh-worn tri-axial accelerometers, and a wrist-worn device during two-minute walk tests in the clinic, with video reference data for validation. Thirteen participants wore one of the thigh-worn tri-axial accelerometers (AP: ActivPAL4) and the wrist-worn device for 7 days under free-living conditions, with proprietary AP data used as reference. Gait events were detected from shank and thigh acceleration using the Teager-Kaiser energy operator combined with unsupervised clustering. Estimated step count (SC) and temporal gait parameters were compared with reference data. In the clinic, low mean absolute percentage errors were observed for stride (shank/thigh: 0.6/0.9%) and stance (shank/thigh: 3.3/7.1%) times, and SC (shank/thigh: 3.1%). Similar errors were observed for proprietary AP SC (3.2%), with higher errors observed for the wrist-worn device (10.9%). At home, excellent agreement was observed between the proposed algorithm and AP software for SC and time spent walking (ICC [Formula: see text]). The wrist-worn device overestimated SC by 34.2%. The presented algorithm additionally allowed stride and stance time estimation, whose variability correlated significantly with clinical motor scores. The results demonstrate a new method for accurate estimation of HD gait parameters in the clinic and free-living conditions, using a single accelerometer worn on either the thigh or shank.
在亨廷顿病（HD）中，可穿戴惯性传感器可以捕捉运动功能的细微变化。然而，有必要对特定于疾病的方法进行验证。本研究提出了一种使用腿部佩戴的加速度计检测 HD 行走回合和步态事件的算法，该算法仅在临床环境中进行了验证，并在自由生活条件下部署。17 名 HD 参与者在临床环境中进行了两分钟步行测试，佩戴了腿部和大腿佩戴的三轴加速度计和手腕佩戴的设备，并使用视频参考数据进行验证。13 名参与者在自由生活条件下佩戴了其中一个大腿佩戴的三轴加速度计（AP：ActivPAL4）和手腕佩戴的设备 7 天，并使用专有的 AP 数据作为参考。使用 Teager-Kaiser 能量运算符结合无监督聚类从腿部和大腿加速度中检测到步态事件。将估计的步数（SC）和时间步态参数与参考数据进行比较。在临床环境中，步幅（腿部/大腿：0.6%/0.9%）和站立时间（腿部/大腿：3.3%/7.1%）和 SC（腿部/大腿：3.1%）的平均绝对百分比误差较低。专有 AP SC 也观察到类似的误差（3.2%），手腕佩戴的设备观察到的误差更高（10.9%）。在家中，所提出的算法与 AP 软件在 SC 和行走时间上的表现出极好的一致性（ICC [公式]）。手腕佩戴的设备高估了 SC 34.2%。该算法还可以估计步幅和站立时间，其变异性与临床运动评分显著相关。结果表明，使用大腿或腿部佩戴的单个加速度计，在临床和自由生活条件下，该算法可以准确估计 HD 步态参数。
