基于数据融合的用于机器人手臂肌电控制的稳健肌电传感系统。
Robust EMG sensing system based on data fusion for myoelectric control of a robotic arm.
作者信息
López Natalia M, di Sciascio Fernando, Soria Carlos M, Valentinuzzi Max E
机构信息
Gabinete de Tecnología Médica, Facultad Ingeniería, Universidad Nacional de San Juan, San Juan, Argentina.
出版信息
Biomed Eng Online. 2009 Feb 25;8:5. doi: 10.1186/1475-925X-8-5.
BACKGROUND
Myoelectric control of a robotic manipulator may be disturbed by failures due to disconnected electrodes, interface impedance changes caused by movements, problems in the recording channel and other various noise sources. To correct these problems, this paper presents two fusing techniques, Variance Weighted Average (VWA) and Decentralized Kalman Filter (DKF), both based on the myoelectric signal variance as selecting criterion.
METHODS
Tested in five volunteers, a redundant arrangement was obtained with two pairs of electrodes for each recording channel. The myoelectric signals were electronically amplified, filtered and digitalized, while the processing, fusion algorithms and control were implemented in a personal computer under MATLAB environment and in a Digital Signal Processor (DSP). The experiments used an industrial robotic manipulator BOSCH SR-800, type SCARA, with four degrees of freedom; however, only the first joint was used to move the end effector to a desired position, the latter obtained as proportional to the EMG amplitude.
RESULTS
Several trials, including disconnecting and reconnecting one electrode and disturbing the signal with synthetic noise, were performed to test the fusion techniques. The results given by VWA and DKF were transformed into joint coordinates and used as command signals to the robotic arm. Even though the resultant signal was not exact, the failure was ignored and the joint reference signal never exceeded the workspace limits.
CONCLUSION
The fault robustness and safety characteristics of a myoelectric controlled manipulator system were substantially improved. The proposed scheme prevents potential risks for the operator, the equipment and the environment. Both algorithms showed efficient behavior. This outline could be applied to myoelectric control of prosthesis, or assistive manipulators to better assure the system functionality when electrode faults or noisy environment are present.
背景
由于电极断开、运动引起的界面阻抗变化、记录通道问题以及其他各种噪声源，机器人操纵器的肌电控制可能会受到故障干扰。为了纠正这些问题，本文提出了两种融合技术，即方差加权平均（VWA）和分散卡尔曼滤波器（DKF），两者均基于肌电信号方差作为选择标准。
方法
在五名志愿者身上进行测试，每个记录通道使用两对电极获得冗余配置。肌电信号经过电子放大、滤波和数字化处理，而处理、融合算法和控制则在MATLAB环境下的个人计算机和数字信号处理器（DSP）中实现。实验使用了具有四个自由度的工业机器人操纵器博世SR - 800（SCARA型）；然而，仅使用第一个关节将末端执行器移动到期望位置，该位置与肌电幅度成比例获得。
结果
进行了几次试验，包括断开和重新连接一个电极以及用合成噪声干扰信号，以测试融合技术。VWA和DKF给出的结果被转换为关节坐标，并用作机器人手臂的命令信号。尽管所得信号并不精确，但故障被忽略，关节参考信号从未超过工作空间限制。
结论
肌电控制操纵器系统的故障鲁棒性和安全特性得到了显著改善。所提出的方案可防止对操作员、设备和环境造成潜在风险。两种算法都表现出高效的性能。当存在电极故障或噪声环境时，此概述可应用于假肢或辅助操纵器的肌电控制，以更好地确保系统功能。
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