Leonardis Daniele, Barsotti Michele, Loconsole Claudio, Solazzi Massimiliano, Troncossi Marco, Mazzotti Claudio, Castelli Vincenzo Parenti, Procopio Caterina, Lamola Giuseppe, Chisari Carmelo, Bergamasco Massimo, Frisoli Antonio
IEEE Trans Haptics. 2015 Apr-Jun;8(2):140-51. doi: 10.1109/TOH.2015.2417570. Epub 2015 Mar 30.
This paper presents a novel electromyography (EMG)-driven hand exoskeleton for bilateral rehabilitation of grasping in stroke. The developed hand exoskeleton was designed with two distinctive features: (a) kinematics with intrinsic adaptability to patient's hand size, and (b) free-palm and free-fingertip design, preserving the residual sensory perceptual capability of touch during assistance in grasping of real objects. In the envisaged bilateral training strategy, the patient's non paretic hand acted as guidance for the paretic hand in grasping tasks. Grasping force exerted by the non paretic hand was estimated in real-time from EMG signals, and then replicated as robotic assistance for the paretic hand by means of the hand-exoskeleton. Estimation of the grasping force through EMG allowed to perform rehabilitation exercises with any, non sensorized, graspable objects. This paper presents the system design, development, and experimental evaluation. Experiments were performed within a group of six healthy subjects and two chronic stroke patients, executing robotic-assisted grasping tasks. Results related to performance in estimation and modulation of the robotic assistance, and to the outcomes of the pilot rehabilitation sessions with stroke patients, positively support validity of the proposed approach for application in stroke rehabilitation.
本文提出了一种用于中风患者抓握双侧康复的新型肌电图（EMG）驱动手部外骨骼。所开发的手部外骨骼具有两个显著特点：（a）运动学上能内在适应患者手部尺寸，（b）自由手掌和自由指尖设计，在辅助抓握真实物体时保留触觉残余感知能力。在设想的双侧训练策略中，患者的非瘫痪手在抓握任务中为瘫痪手提供指导。通过肌电图信号实时估计非瘫痪手施加的抓握力，然后借助手部外骨骼将其复制为对瘫痪手的机器人辅助。通过肌电图估计抓握力可使用任何无传感器的可抓握物体进行康复训练。本文介绍了系统设计、开发和实验评估。在一组六名健康受试者和两名慢性中风患者中进行了实验，执行机器人辅助抓握任务。与机器人辅助的估计和调节性能以及中风患者试点康复疗程结果相关的结果，积极支持了所提出方法在中风康复中应用的有效性。
