Abstract
Rehabilitation robots play an increasingly important role in the recovery of motor function for stroke. To ensure a natural physical human-robot interaction (pHRI) and enhance the active participation of subjects, it is necessary for the robots to understand the human intention and cooperate actively with humanlike characteristics. This study proposed a hybrid active control algorithm with human motion intention detection. The motion intention was defined as the desired position and velocity, which were continuously estimated according to the human upper-limb model and minimum jerk model, respectively. The motion intention was then fed into a hybrid force and position controller of an upper-limb cable driven rehabilitation robot (CDRR). And a three-dimensional reaching task without predefined trajectory was employed to validate the effectiveness of the proposed control algorithm. Experimental results showed that the control algorithm could continuously recognize the human motion intention and enabled the robot better movement performance indicated as smaller offset error, smoother trajectory, and lower impact. The proposed method could guarantee a natural pHRI and improve the engagement of the subjects, which has great potential in clinical applications.
Highlights
Stroke has become a leading threat to human health worldwide
A variable admittance control method based on human intention, environmental force, and environment stiffness was proposed by Li et al to address physical human-robot interaction (pHRI) which was coupled with an environment of VOLUME XX, 2017
The good tracking performance of both AC-Minimum Jerk model (MJM) and ACnMJM in this paper indicated that a control algoxierithm with human intention detection could help subjects fulfill designed tasks with his or her active effort
Summary
Stroke has become a leading threat to human health worldwide. Its high disability rate has caused a great many patients suffering from motor dysfunction and unable to carry out daily activities [1]. Researches have showed that high-intensity rehabilitation training is significant for motor function recovery [2], [3]. For the liberation of physical therapist and accessibility of high-intensity training, rehabilitation robots play an increasingly important role in motor function recovery. Since rehabilitation robots usually physically interact with human body, the implemented control algorithm should be carefully designed. The impedance control scheme proposed by Hogan [8] has been a promising method for physical human-robot interaction (pHRI). The impedance control scheme is a force controller with a position feedback, and its dual admittance control is a model-based position controller adjusting the predefined trajectory based on the interaction force between the robot and environment.
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