Kopke Joseph V, Ellis Michael D, Hargrove Levi J
Departments of Physical Therapy and Human Movement Sciences and Biomedical Engineering at Northwestern University and with the Center for Bionic Medicine at the Shirley Ryan Ability Lab, Chicago, IL 60611 USA (phone: 312-908-8160; fax: 312-908-0741.
Associate Professor with the Departments of Physical Therapy and Human Movement Sciences and Physical Medicine and Rehabilitation at Northwestern University, Chicago, IL, 60611, USA.
Proc IEEE RAS EMBS Int Conf Biomed Robot Biomechatron. 2020 Nov-Dec;2020:833-838. doi: 10.1109/biorob49111.2020.9224395. Epub 2020 Oct 15.
Stroke often results in chronic motor impairment of the upper-extremity yet neither traditional- nor robotics-based therapy has been able to affect this in a profound way. Supporting the weak affected shoulder against gravity improves reaching distance and minimizes abnormal co-contraction of the elbow, wrist, and fingers after stroke. However, it is necessary to assess the feasibility and efficacy of real-time controllers for this population as technology advances and a wearable shoulder device comes closer to reality. The aim of this study is to test two EMG-based controllers in this regard. A linear discriminant analysis based classifier was trained using extracted time domain and auto-regressive features from electromyographic data acquired during muscle effort required to move a load equivalent to 50 and 100% limb weight (abduction) and 150 and 200% limb weight (adduction). While rigidly connected to a custom lab-based robot, the participant was required to complete a series of lift and reach tasks under two different control paradigms: position-based control and force-based control. The participant successfully controlled the robot under both paradigms as indicated by first moving the robot arm into the proper vertical window and then reaching out as far as possible while remaining within the vertical window. This case study begins to assess the feasibility of using electromyographic data to classify the intended shoulder movement of a participant with stroke during a functional lift and reach type task. Next steps will assess how this type of support affects reaching function.
中风常常导致上肢慢性运动功能障碍，但传统疗法和基于机器人的疗法都未能对此产生深远影响。在中风后，支撑受影响的虚弱肩部对抗重力可增加够物距离，并使肘部、腕部和手指的异常协同收缩最小化。然而，随着技术的进步以及可穿戴肩部设备日益接近现实，有必要评估针对这一人群的实时控制器的可行性和有效性。本研究的目的是在这方面测试两种基于肌电图的控制器。使用从在移动相当于50%和100%肢体重量（外展）以及150%和200%肢体重量（内收）的负荷所需的肌肉用力过程中采集的肌电数据中提取的时域和自回归特征，训练了一种基于线性判别分析的分类器。在与一个基于实验室的定制机器人刚性连接的情况下，要求参与者在两种不同的控制范式下完成一系列提起和够物任务：基于位置的控制和基于力的控制。如先将机器人手臂移动到适当的垂直窗口，然后在保持在垂直窗口内的同时尽可能伸展所表明的，参与者在两种范式下都成功地控制了机器人。本案例研究开始评估在功能性提起和够物类型任务中使用肌电数据对中风参与者的预期肩部运动进行分类的可行性。后续步骤将评估这种类型的支撑如何影响够物功能。
