As the primary effector of human, hand is dexterous, important, and biomechanically complex. How to control the robotic hand as human intends in the human-centered robotic systems is a hot topic to guarantee natural interaction between users and robots. In this article, we investigate the simultaneous and proportional estimation of human's movement intent from surface electromyography (EMG) signals. A sparse pseudo-input Gaussian process regression method is proposed to map the EMG features and the hand kinematics. The kinematics of five primary degrees of freedom (DoFs) are estimated from EMG recorded during functional grasping tasks. From the experiments on eight able-bodied subjects, ipsi-lateral and contra-lateral training strategies represent similar estimation accuracy (CC = 0.89 and 0.88, respectively) and no significant difference ( p = 0.8) between them. The online estimation with contra-lateral strategy shows higher average estimation accuracy (CC = 0.91). In addition, the estimation of the intended kinematics can be decoded in real time with negligible response delays. It is suggested to apply the proposed method to multi-DoF decoding for natural, intuitive, and accurate myocontrol of robotic hands.

中文翻译：

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基于肌电信号的多关节运动学同时和比例估计，用于机器人手的肌肉控制

作为人类的主要效应器，手灵巧，重要且生物力学复杂。如何在以人为中心的机器人系统中按照人的意图控制机器人手是确保用户与机器人之间自然交互的热门话题。在本文中，我们研究了根据表面肌电图（EMG）信号同时并成比例地估计人的运动意图。提出了一种稀疏的伪输入高斯过程回归方法来映射肌电特征和手运动学。五个主要自由度（DoF）的运动学是根据在功能抓取任务期间记录的EMG估算的。通过对八个健全主体的实验，同侧和对侧训练策略表示相似的估计准确度（分别为CC = 0.89和0.88），并且无显着差异（ p= 0.8）。使用对侧策略进行的在线估算显示出更高的平均估算精度（CC = 0.91）。另外，可以以可忽略的响应延迟实时解码预期的运动学估计。建议将提出的方法应用于多自由度解码，以实现对机器人手的自然，直观和准确的肌肉控制。