Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:4101-4104. doi: 10.1109/EMBC48229.2022.9871724.
Motor function evaluation plays an important role in post-stroke rehabilitation. However, the traditional evaluation is subjective and laborious, which may bring a heavy burden to both physicians and stroke survivors. Therefore, an automatic and objective rehabilitation evaluation is needed to minimize the burden of physician, so as to achieve a simplified and objective evaluation process. The main purpose of this study is to investigate the minimum number of tasks for upper-extremity actions in objective assessment of stroke survivors with a Brunnstrom stage (BS) based on wearable sensing device, which can achieve a satisfactory result to reduce the burden of stroke survivors. In this study, we employed 20 stroke survivors and 7 healthy participants, performing three types of daily living activities (drinking, teeth brushing, face washing). The acceleration, angular velocity and surface Electromyography signals on five parts of the forearm were simultaneously acquired. Then, we compared the effects of each action combination under multiple classifiers. The results show that the use of a single action can achieve competitive results compared with multiple action combination classifications, and the use of K nearest neighbor (KNN) algorithm for the average recognition accuracy of face washing action shows better performance, with the highest accuracy reaching 85.65±6.21% (mean ± standard error), 23 of the 27 subjects were accurately classified. These findings indicate that the predominant qualitative assessment after stroke can be supplemented by corresponding quantitative solutions, and that stroke rehabilitation can be automated with less professional therapist involvement.
运动功能评估在脑卒中康复中起着重要作用。然而，传统的评估方法既主观又费力，可能会给医生和脑卒中幸存者带来沉重的负担。因此，需要一种自动和客观的康复评估方法，以最大限度地减轻医生的负担，从而实现简化和客观的评估过程。本研究的主要目的是探讨基于可穿戴传感设备的脑卒中幸存者 Brunnstrom 阶段（BS）上肢动作的最小任务数，以达到令人满意的结果，减轻脑卒中幸存者的负担。在这项研究中，我们招募了 20 名脑卒中幸存者和 7 名健康参与者，让他们进行三种日常生活活动（喝水、刷牙、洗脸）。同时采集前臂五个部位的加速度、角速度和表面肌电信号。然后，我们比较了在多个分类器下每种动作组合的效果。结果表明，与多动作组合分类相比，单一动作的使用可以达到具有竞争力的结果，而 K 最近邻（KNN）算法对面部清洗动作的平均识别准确率表现出更好的性能，最高准确率达到 85.65±6.21%（平均值±标准误差），27 名受试者中有 23 名被准确分类。这些发现表明，脑卒中后的定性评估可以辅以相应的定量解决方案，并且可以通过减少专业治疗师的参与来实现脑卒中康复的自动化。
