The purpose of this paper is to propose a seamless active interaction control method integrating electromyography (EMG)-triggered assistance and the adaptive impedance control scheme for parallel robot-assisted lower limb rehabilitation and training.
An active interaction control strategy based on EMG motion recognition and adaptive impedance model is implemented on a six-degrees of freedom parallel robot for lower limb rehabilitation. The autoregressive coefficients of EMG signals integrating with a support vector machine classifier are utilized to predict the movement intention and trigger the robot assistance. An adaptive impedance controller is adopted to influence the robot velocity during the exercise, and in the meantime, the user’s muscle activity level is evaluated online and the robot impedance is adapted in accordance with the recovery conditions.
Experiments on healthy subjects demonstrated that the proposed method was able to drive the robot according to the user’s intention, and the robot impedance can be updated with the muscle conditions. Within the movement sessions, there was a distinct increase in the muscle activity levels for all subjects with the active mode in comparison to the EMG-triggered mode.
Both users’ movement intention and voluntary participation are considered, not only triggering the robot when people attempt to move but also changing the robot movement in accordance with user’s efforts. The impedance model here responds directly to velocity changes, and thus allows the exercise along a physiological trajectory. Moreover, the muscle activity level depends on both the normalized EMG signals and the weight coefficients of involved muscles.
This research is funded and supported by the National Natural Science Foundation of China (Grant No. 51475342) and the Fundamental Research Funds for the Central Universities (Grant Nos. 2013-YB-002 and 2013-IV-129).
Meng, W., Liu, Q., Zhou, Z. and Ai, Q. (2014), "Active interaction control applied to a lower limb rehabilitation robot by using EMG recognition and impedance model", Industrial Robot, Vol. 41 No. 5, pp. 465-479. https://doi.org/10.1108/IR-04-2014-0327Download as .RIS
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