Wang Dongqing, Zhang Xu, Chen Xiang, Zhou Ping
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:3578-81. doi: 10.1109/EMBC.2014.6944396.
Myoelectric pattern recognition applied to high-density surface electromyographic (sEMG) recordings from paretic muscles has been proven to identify various movement intents of stroke survivors, thus facilitating the design of myoelectrically controlled robotic systems for recovery of upper-limb dexterity. Aiming at effectively decoding neural control information under the condition of neurological injury following stroke, this paper further investigates the application of wavelet packet transform (WPT) on myoelectric feature extraction to identify 20 functional movements performed by the paretic upper limb of 4 chronic stroke subjects. The WPT was used to decompose the original sEMG signals via a tree of subspaces, where optimal ones were selected in term of the classification efficacy. The energies in the selected subspaces were calculated as optimal wavelet packet features, which were finally fed into a linear discriminant classifier. The WPT-based myoelectric feature extraction approach achieved accuracies above 94% for all subjects in a user-specific condition, demonstrating its potential applications in upper limb rehabilitation after stroke.
肌电模式识别应用于中风患者患侧肌肉的高密度表面肌电图（sEMG）记录，已被证明可识别中风幸存者的各种运动意图，从而有助于设计用于恢复上肢灵活性的肌电控制机器人系统。为了在中风后神经损伤的情况下有效解码神经控制信息，本文进一步研究小波包变换（WPT）在肌电特征提取中的应用，以识别4名慢性中风患者患侧上肢执行的20种功能性运动。WPT用于通过子空间树分解原始sEMG信号，根据分类效果选择最优子空间。所选子空间中的能量被计算为最优小波包特征，最终输入线性判别分类器。基于WPT的肌电特征提取方法在用户特定条件下对所有受试者的准确率均高于94%，证明了其在中风后上肢康复中的潜在应用价值。
