Department of Physical Therapy, Crean College of Health and Behavioral Sciences, Chapman University, 9401 Jeronimo Rd, Irvine, CA, 92618, USA.
Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN, USA.
J Neuroeng Rehabil. 2022 May 7;19(1):44. doi: 10.1186/s12984-022-01020-8.
Individuals with hemiparesis post-stroke often have difficulty with tasks requiring upper extremity (UE) intra- and interlimb use, yet methods to quantify both are limited.
To develop a quantitative yet sensitive method to identify distinct features of UE intra- and interlimb use during task performance.
Twenty adults post-stroke and 20 controls wore five inertial sensors (wrists, upper arms, sternum) during 12 seated UE tasks. Three sensor modalities (acceleration, angular rate of change, orientation) were examined for three metrics (peak to peak amplitude, time, and frequency). To allow for comparison between sensor data, the resultant values were combined into one motion parameter, per sensor pair, using a novel algorithm. This motion parameter was compared in a group-by-task analysis of variance as a similarity score (0-1) between key sensor pairs: sternum to wrist, wrist to wrist, and wrist to upper arm. A use ratio (paretic/non-paretic arm) was calculated in persons post-stroke from wrist sensor data for each modality and compared to scores from the Adult Assisting Hand Assessment (Ad-AHA Stroke) and UE Fugl-Meyer (UEFM).
A significant group × task interaction in the similarity score was found for all key sensor pairs. Post-hoc tests between task type revealed significant differences in similarity for sensor pairs in 8/9 comparisons for controls and 3/9 comparisons for persons post stroke. The use ratio was significantly predictive of the Ad-AHA Stroke and UEFM scores for each modality.
Our algorithm and sensor data analyses distinguished task type within and between groups and were predictive of clinical scores. Future work will assess reliability and validity of this novel metric to allow development of an easy-to-use app for clinicians.
脑卒中后偏瘫患者在完成需要上肢（UE）内、肢体间使用的任务时常常存在困难，但量化这些任务的方法有限。
开发一种定量且敏感的方法，以识别上肢内、肢体间使用在任务执行过程中的独特特征。
20 名脑卒中后患者和 20 名对照组在 12 项坐姿 UE 任务中佩戴 5 个惯性传感器（手腕、上臂、胸骨）。检查了 3 种传感器模态（加速度、角速率变化、方向），并对 3 种度量（峰峰值幅度、时间和频率）进行了研究。为了允许在传感器数据之间进行比较，使用一种新的算法将每个传感器对的结果值组合成一个运动参数。在组间任务方差分析中，将该运动参数作为关键传感器对（胸骨至手腕、手腕至手腕和手腕至上臂）之间的相似性评分（0-1）进行比较。在脑卒中患者中，从手腕传感器数据为每种模态计算了使用比例（患侧/非患侧手臂），并与成人辅助手评估（Ad-AHA 卒中）和 UE 弗格伦迈尔（UEFM）的评分进行了比较。
在所有关键传感器对中都发现了组间任务交互的显著差异。任务类型的事后检验显示，在对照组的 8/9 个比较中，以及在脑卒中患者的 3/9 个比较中，传感器对的相似性存在显著差异。对于每种模态，使用比例与 Ad-AHA 卒中和 UEFM 评分显著相关。
我们的算法和传感器数据分析能够区分组内和组间的任务类型，并能够预测临床评分。未来的工作将评估这种新度量的可靠性和有效性，以开发一种易于使用的临床应用程序。
