拇指
肌电图
主成分分析
电机单元
前臂
计算机科学
神经生理学
可穿戴计算机
背
神经科学
模式识别(心理学)
物理医学与康复
人工智能
解剖
心理学
生物
医学
嵌入式系统
作者
Simone Tanzarella,Silvia Muceli,Alessandro Del Vecchio,Francesco Casolo,Dario Farina
标识
DOI:10.1109/embc.2019.8856825
摘要
We propose a framework based on high-density surface electromyography (HD-sEMG) to identify the neural drive to muscles controlling the human hand. High-density (320 channels) sEMG signals were recorded concurrently from intrinsic (the four dorsal interossei and thenar) and extrinsic (forearm) hand muscles and then decomposed into the constituent trains of motor unit (MU) action potentials. The participants performed pinch tasks with simultaneous activation of the thumb and one of the other fingers with sinusoidal force variations. The common drive among MUs across different muscles was extracted via principal component analysis (PCA) of the smoothed MU discharge rates. The first principal component of the smoothed discharge rates of all identified motor neurons explained 48.7 ± 15.4% of the total variance across all pinching tasks, indicating a common neural input shared by different muscles of the forearm and the hand.. When considering only the MUs extracted from extrinsic and intrinsic muscles, the percent of variance explained was 48.3 ± 15.3% and 57.1 ± 15.5%, respectively. This framework is conceived to use motor neuron activity for a proportional myoelectric control and rehabilitation technologies. A wearable adaptation of the framework is proposed for future perspectives.
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