Ngeo Jimson, Tamei Tomoya, Ikeda Kazushi, Shibata Tomohiro
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:2095-8. doi: 10.1109/EMBC.2015.7318801.
Accurate proportional myoelectric control of the hand is important in replicating dexterous manipulation in robot prostheses and orthoses. However, this is still difficult to achieve due to the complex and high degree-of-freedom (DOF) nature present in the governing musculoskeletal system. To address this problem, we suggest using a low dimensional encoding based on nonlinear synergies to represent both the high-DOF finger joint kinematics and the coordination of muscle activities taken from surface electromyographic (EMG) signals. Generating smooth multi-finger movements using EMG inputs is then done by using a shared Gaussian Process latent variable model that learns a dynamical model between both the kinematic and EMG data represented in a shared latent space. The experimental results show that the method is able to synthesize continuous movements of a full five-finger hand model, with total dimensions as large as 69 (although highly redundant and correlated). Finally, by comparing the estimation performances when the number of EMG latent dimensions are varied, we show that these synergistic features can capture the variance, shared and specific to the observed kinematics.
对手部进行精确的比例肌电控制对于在机器人假肢和矫形器中复制灵巧操作至关重要。然而，由于支配肌肉骨骼系统具有复杂且高自由度（DOF）的特性，这一点仍难以实现。为了解决这个问题，我们建议使用基于非线性协同作用的低维编码来表示高自由度的手指关节运动学以及从表面肌电图（EMG）信号中获取的肌肉活动协调。然后，通过使用共享高斯过程潜在变量模型来生成基于EMG输入的平滑多指运动，该模型学习在共享潜在空间中表示的运动学和EMG数据之间的动态模型。实验结果表明，该方法能够合成一个完整的五指手模型的连续运动，其总维度高达69（尽管存在高度冗余和相关性）。最后，通过比较当EMG潜在维度数量变化时的估计性能，我们表明这些协同特征可以捕获观察到的运动学所特有的、共享的方差。
