Bakshi Koushik, Pramanik Rajesh, Manjunatha M, Kumar C S
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2024-2027. doi: 10.1109/EMBC.2018.8512678.
This study described the use of Kernel Least Square Tracker based estimation for 3-dimensional shoulder, elbow motion kinematics from surface Electromyogram (EMG) and a two-stage multiclass Support Vector Machine based classification of different wrist, grip and finger motions from Electroencephalogram (EEG). The advantage of employing hybrid EEG-EMG strategy for upper limb motion estimation was demonstrated for a transhumeral subject. The method utilized EMG from upper arm muscles for elbow motion (and shoulder motion in case of higher degree amputation scenario) and used EEG for discerning basic wrist, grip and finger motions. The results showed that the hybrid scheme could estimate shoulder, elbow motion with more than 90% accuracy and wrist, grip and finger motion with 65%-70% accuracy. This strategy of using hybrid EEG-EMG motion estimation, thus, could be employed in developing a more intuitive upper limb prosthesis controller with multiple degrees of freedom.
本研究描述了基于核最小二乘跟踪器的估计方法在从表面肌电图（EMG）获取三维肩部、肘部运动运动学方面的应用，以及基于两阶段多类支持向量机对来自脑电图（EEG）的不同手腕、抓握和手指运动进行分类的方法。对于一名经肱骨截肢的受试者，展示了采用混合EEG-EMG策略进行上肢运动估计的优势。该方法利用上臂肌肉的EMG来估计肘部运动（在高位截肢情况下还可估计肩部运动），并使用EEG来识别基本的手腕、抓握和手指运动。结果表明，混合方案能够以超过90%的准确率估计肩部、肘部运动，以65%-70%的准确率估计手腕、抓握和手指运动。因此，这种使用混合EEG-EMG运动估计的策略可用于开发更直观的多自由度上肢假肢控制器。
