Classifying Unimanual and Bimanual Upper Extremity Tasks in Individuals Post-Stroke
Classifying Unimanual and Bimanual Upper Extremity Tasks in Individuals Post-Stroke
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DOI：
10.1109/embc46164.2021.9629885
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Aaron Miller;Eric Wade
中科院分区：
文献类型：
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作者：
Aaron Miller;Eric Wade
中风后，许多人会出现导致代偿性运动的损伤。补偿允许个人完成任务，但会产生长期有害影响，并代表不适应的运动策略。增加双手运动的使用可以作为恢复的生物标志（以及减少对代偿性运动的依赖），并且使用传感器数据跟踪此类运动可以为医疗保健专家提供关键数据。然而，作者过去的工作表明，运动策略的个体差异会导致传感器数据嘈杂和混乱。当前工作的目标是开发能够使用可穿戴传感器数据区分单手、双手不对称和双手对称手势的分类器。 20 名中风后参与者（以及 20 名年龄匹配的对照组）在训练有素的职业治疗师的监督下执行了一组任务。记录每个任务的传感器数据。分类器是使用人工神经网络 (ANN) 作为基线和回声状态神经网络 (ESNN) 开发的，该网络已证明对混沌数据的有效性。我们发现，对于对照组和中风后参与者，ESNN 提高了测试准确性（分别为 91.3% 和 80.3%）。这些结果提出了一种对中风后个体的姿势进行分类的新方法，并且开发的分类器可以促进补偿性运动的纵向监测和校正。
After stroke, many individuals develop impairments that lead to compensatory motions. Compensation allows individuals to achieve tasks but has long-term detrimental effects and represents maladaptive motor strategies. Increased use of bimanual motions may serve as a biomarker for recovery (and the reduction of reliance on compensatory motion), and tracking such motion using sensor data may provide critical data for health care specialists. However, past work by the authors demonstrated individual variation in motor strategies results in noisy and chaotic sensor data. The goal of the current work is to develop classifiers capable of differentiating unimanual, bimanaual asymmetric, and bimanual symmetric gestures using wearable sensor data. Twenty participants post-stroke (and 20 age-matched controls) performed a set of tasks under the supervision of a trained occupational therapist. Sensor data were recorded for each task. Classifiers were developed using artificial neural networks (ANNs) as a baseline, and the echo state neural network (ESNN) which has demonstrated efficacy with chaotic data. We find that, for control and post-stroke participants, the ESNN results in improved testing accuracy performance (91.3% and 80.3%, respectively). These results suggest a novel method for classifying gestures in individuals post-stroke, and the developed classifiers may facilitate longitudinal monitoring and correction of compensatory motion.