Tanzarella Simone, Di Domenico Dario, Forsiuk Inna, Boccardo Nicolò, Chiappalone Michela, Bartolozzi Chiara, Semprini Marianna
Event-Driven Perception, Italian Institute of Technology, Via San Quirico, 19, 16163 Genova, GE, Italy.
Rehab Technologies Lab, Italian Institute of Technology, Via Morego, 30, 16163 Genova, GE, Italy.
J Neural Eng. 2024 Apr 15;21(2). doi: 10.1088/1741-2552/ad38dd.
We analyze and interpret arm and forearm muscle activity in relation with the kinematics of hand pre-shaping during reaching and grasping from the perspective of human synergistic motor control.Ten subjects performed six tasks involving reaching, grasping and object manipulation. We recorded electromyographic (EMG) signals from arm and forearm muscles with a mix of bipolar electrodes and high-density grids of electrodes. Motion capture was concurrently recorded to estimate hand kinematics. Muscle synergies were extracted separately for arm and forearm muscles, and postural synergies were extracted from hand joint angles. We assessed whether activation coefficients of postural synergies positively correlate with and can be regressed from activation coefficients of muscle synergies. Each type of synergies was clustered across subjects.We found consistency of the identified synergies across subjects, and we functionally evaluated synergy clusters computed across subjects to identify synergies representative of all subjects. We found a positive correlation between pairs of activation coefficients of muscle and postural synergies with important functional implications. We demonstrated a significant positive contribution in the combination between arm and forearm muscle synergies in estimating hand postural synergies with respect to estimation based on muscle synergies of only one body segment, either arm or forearm (< 0.01). We found that dimensionality reduction of multi-muscle EMG root mean square (RMS) signals did not significantly affect hand posture estimation, as demonstrated by comparable results with regression of hand angles from EMG RMS signals.We demonstrated that hand posture prediction improves by combining activity of arm and forearm muscles and we evaluate, for the first time, correlation and regression between activation coefficients of arm muscle and hand postural synergies. Our findings can be beneficial for myoelectric control of hand prosthesis and upper-limb exoskeletons, and for biomarker evaluation during neurorehabilitation.
我们从人类协同运动控制的角度,分析和解释了在伸手和抓握过程中,手臂和前臂肌肉活动与手部预塑形运动学之间的关系。十名受试者完成了六项涉及伸手、抓握和物体操作的任务。我们使用双极电极和高密度电极网格混合记录了手臂和前臂肌肉的肌电图(EMG)信号。同时记录运动捕捉数据以估计手部运动学。分别提取了手臂和前臂肌肉的肌肉协同作用,并从手部关节角度提取了姿势协同作用。我们评估了姿势协同作用的激活系数是否与肌肉协同作用的激活系数呈正相关,以及是否可以从肌肉协同作用的激活系数中进行回归分析。每种类型的协同作用都在受试者之间进行了聚类。我们发现受试者之间所识别的协同作用具有一致性,并且我们对跨受试者计算的协同作用聚类进行了功能评估,以识别代表所有受试者的协同作用。我们发现肌肉和姿势协同作用的激活系数对之间存在正相关,具有重要的功能意义。相对于仅基于一个身体部位(手臂或前臂)的肌肉协同作用进行的估计,我们证明了手臂和前臂肌肉协同作用的组合在估计手部姿势协同作用方面具有显著的正向贡献(<0.01)。我们发现,多肌肉EMG均方根(RMS)信号的降维对手部姿势估计没有显著影响,这通过从EMG RMS信号回归手部角度的可比结果得到了证明。我们证明了通过结合手臂和前臂肌肉的活动可以改善手部姿势预测,并且我们首次评估了手臂肌肉激活系数与手部姿势协同作用之间的相关性和回归分析。我们的研究结果可能有助于手部假肢和上肢外骨骼的肌电控制,以及神经康复期间的生物标志物评估。