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一种用于部署同时肌电控制的三自由度半约束手腕运动/力检测装置。

A 3-DOF hemi-constrained wrist motion/force detection device for deploying simultaneous myoelectric control.

机构信息

State Key Laboratory of Robotics and System (SKLRS), Harbin Institute of Technology, #3039, JQR Building, NO.2 Yikuang Str., Harbin, 150080, China.

Artificial Intelligence Research (HAI), Harbin Institute of Technology, NO.92 Xidazhi Str., Harbin, 150001, China.

出版信息

Med Biol Eng Comput. 2018 Sep;56(9):1669-1681. doi: 10.1007/s11517-018-1807-2. Epub 2018 Mar 5.

Abstract

For describing the state of the wrist, either the force or movement of wrist can be measured as the training target in the simultaneous electromyography control. However, the relationship between the force and movement is so complex that only the force or movement is not precise enough to describe its actual situations. In this paper, we propose a novel platform that can acquire three degrees of freedom (DOF) wrist motion/force synchronously with multi-channel electromyography signals in a hemi-constraint way. The self-made wrist force-movement mapping device establishes a stable relationship between the wrist movement and force. Meanwhile, the elicited wrist movement can be directly fed back to the subjects via laser cursor. The information of the cursor can directly reflect the 3-DOF movement of the wrist without any decoupling algorithms. Through this platform, the support vector regression model learned from the training data can well predict the arbitrary combinations of 3-DOF wrist movements. The cross-validation result indicates that the regression accuracy of free 3-DOF movements can reach a similar performance to that of 2-DOF regular movements (in terms of R, regular movement vs. free movement, p > 0.1). Graphical abstract The hemi-constrained platform used for detecting 3-DOF wrist movements.

摘要

为了描述手腕的状态,可以将手腕的力或运动作为同时肌电图控制的训练目标。然而,力和运动之间的关系非常复杂,仅用力或运动不足以精确描述其实际情况。在本文中,我们提出了一种新的平台,可以以半约束的方式同步获取三自由度(DOF)手腕运动/力和多通道肌电图信号。自制的手腕力-运动映射装置建立了手腕运动和力之间的稳定关系。同时,诱发的手腕运动可以通过激光光标直接反馈给受试者。光标信息可以直接反映手腕的 3-DOF 运动,无需任何解耦算法。通过这个平台,从训练数据中学习到的支持向量回归模型可以很好地预测任意组合的 3-DOF 手腕运动。交叉验证结果表明,自由 3-DOF 运动的回归精度可以达到与 2-DOF 规则运动相似的性能(就 R 而言,规则运动与自由运动,p>0.1)。

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