Semprini Marianna, Cuppone Anna Vera, Delis Ioannis, Squeri Valentina, Panzeri Stefano, Konczak Jurgen
IEEE Trans Neural Syst Rehabil Eng. 2017 Jul;25(7):883-892. doi: 10.1109/TNSRE.2016.2636122. Epub 2016 Dec 6.
Electrophysiological recordings from human muscles can serve as control signals for robotic rehabilitation devices. Given that many diseases affecting the human sensorimotor system are associated with abnormal patterns of muscle activation, such biofeedback can optimize human-robot interaction and ultimately enhance motor recovery. To understand how mechanical constraints and forces imposed by a robot affect muscle synergies, we mapped the muscle activity of seven major arm muscles in healthy individuals performing goal-directed discrete wrist movements constrained by a wrist robot. We tested six movement directions and four force conditions typically experienced during robotic rehabilitation. We analyzed electromyographic (EMG) signals using a space-by-time decomposition and we identified a set of spatial and temporal modules that compactly described the EMG activity and were robust across subjects. For each trial, coefficients expressing the strength of each combination of modules and representing the underlying muscle recruitment, allowed for a highly reliable decoding of all experimental conditions. The decomposition provides compact representations of the observable muscle activation constrained by a robotic device. Results indicate that a low-dimensional control scheme incorporating EMG biofeedback could be an effective add-on for robotic rehabilitative protocols seeking to improve impaired motor function in humans.
来自人体肌肉的电生理记录可作为机器人康复设备的控制信号。鉴于许多影响人体感觉运动系统的疾病都与肌肉激活模式异常有关，这种生物反馈可以优化人机交互，并最终促进运动恢复。为了了解机器人施加的机械约束和力如何影响肌肉协同作用，我们绘制了在手腕机器人约束下进行目标导向离散手腕运动的健康个体中七块主要手臂肌肉的活动情况。我们测试了机器人康复过程中通常会遇到的六个运动方向和四种力的条件。我们使用时空分解分析肌电图（EMG）信号，并识别出一组紧凑描述EMG活动且在不同受试者间具有稳健性的时空模块。对于每个试验，表达模块每种组合强度并代表潜在肌肉募集的系数，能够对所有实验条件进行高度可靠的解码。这种分解提供了由机器人设备约束的可观察肌肉激活的紧凑表示。结果表明，结合EMG生物反馈的低维控制方案可能是寻求改善人类受损运动功能的机器人康复方案的有效附加手段。
