El-Khoury S, Batzianoulis I, Antuvan C W, Contu S, Masia L, Micera S, Billard A
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:6732-5. doi: 10.1109/EMBC.2015.7319938.
This paper proposes an EMG based learning approach for estimating the displacement along the 2-axes (abduction/adduction and flexion/extension) of the human wrist in real-time. The algorithm extracts features from the EMG electrodes on the upper and forearm and uses Support Vector Regression to estimate the intended displacement of the wrist. Using data recorded with the arm outstretched in various locations in space, we train the algorithm so as to allow robust prediction even when the subject moves his/her arm across several positions in space. The proposed approach was tested on five healthy subjects and showed that a R(2) index of 63.6% is obtained for generalization across different arm positions and wrist joint angles.
本文提出了一种基于肌电图（EMG）的学习方法，用于实时估计人类手腕在两个轴向上（外展/内收和屈曲/伸展）的位移。该算法从上肢和前臂的肌电电极中提取特征，并使用支持向量回归来估计手腕的预期位移。利用在空间中不同位置伸展手臂时记录的数据，我们对该算法进行训练，以便即使受试者将手臂在空间中移动到多个位置时也能进行稳健的预测。该方法在五名健康受试者身上进行了测试，结果表明，在不同手臂位置和腕关节角度的情况下，泛化的R(2)指数为63.6%。
