One of the most frequent and severe aftermaths of a stroke is the loss of upper limb functionality. Therapy started in the sub-acute phase proved more effective, mainly when the patient participates actively. Recently, a novel set of rehabilitation and support robotic devices, known as supernumerary robotic limbs, have been introduced. This work investigates how a surface electromyography (sEMG) based control strategy would improve their usability in rehabilitation, limited so far by input interfaces requiring to subjects some level of residual mobility. After briefly introducing the phenomena hindering post-stroke sEMG and its use to control robotic hands, we describe a framework to acquire and interpret muscle signals of the forearm extensors. We applied it to drive a supernumerary robotic limb, the SoftHand-X, to provide Task-Specific Training (TST) in patients with sub-acute stroke. We propose and describe two algorithms to control the opening and closing of the robotic hand, with different levels of user agency and therapist control. We experimentally tested the feasibility of the proposed approach on four patients, followed by a therapist, to check their ability to operate the hand. The promising preliminary results indicate sEMG-based control as a viable solution to extend TST to sub-acute post-stroke patients.

翻译：脑卒中最常见且最严重的后遗症之一是上肢功能丧失。在亚急性期开始治疗被证明更为有效，尤其是在患者积极参与的情况下。近年来，一类新型康复与辅助机器人设备——超数机器人肢体——已被引入。本研究探讨了基于表面肌电图(sEMG)的控制策略如何改善其康复可用性（该可用性此前受限于需要受试者具备一定程度残余活动能力的输入接口）。在简要介绍阻碍脑卒中后sEMG信号获取及其用于控制机器人手部的现象后，我们描述了一个用于采集和解读前臂伸肌肌肉信号的框架。我们将该框架应用于驱动超数机器人肢体SoftHand-X，为亚急性脑卒中患者提供任务特异性训练(TST)。我们提出并描述了两种控制机器人手部开合(具有不同层级用户自主性和治疗师控制)的算法。我们通过四名患者在治疗师陪同下进行实验，测试了所提方法的可行性，以检验其操作手部的能力。初步结果令人鼓舞，表明基于sEMG的控制是扩展TST至亚急性脑卒中后患者的可行方案。