Abstract
This paper proposes a new robot controller for motor-system rehabilitation. The proposed controller simultaneously realizes rehabilitation motion tracking and force generation, as predefined through a musculoskeletal model-based optimization process. We introduce control parameters of weighted control action priorities for the motion-tracking and force-generation tasks, based on the position-tracking error. With the weighted control action priorities, the robot accords higher priority to motion tracking at the robot end point when the position-tracking error is larger than a threshold value, and to force generation when the position-tracking error is smaller than a threshold value. Smooth motion trajectory has to be designed and applied in robot-based rehabilitation. Through simulations and experimental results, we show the usefulness of the proposed control method.
Highlights
Robots are expected to supplement and provide alterna‐ tives in the current roles of physiotherapists in rehabilita‐ tion centres, allowing patients to conduct rehabilitation tasks in an accurate and stable manner, since robot-aided rehabilitation includes tracking of complex motion and generation of force at the end point, fast time response characteristics, repeatability of behaviour, and quantitative evaluation of patient motion by the use of a force and/or motion sensor
A similar strategy can be applied to the rehabilitation robots: when the position error is larger than a threshold value, we can apply position-mode impedance control with high impedance field around the desired end point of the robot
In order to confirm the usefulness of the proposed active rehabilitation method, we show the experimental results
Summary
Robots are expected to supplement and provide alterna‐ tives in the current roles of physiotherapists in rehabilita‐ tion centres, allowing patients to conduct rehabilitation tasks in an accurate and stable manner, since robot-aided rehabilitation includes tracking of complex motion and generation of force at the end point, fast time response characteristics, repeatability of behaviour, and quantitative evaluation of patient motion by the use of a force and/or motion sensor. A gait-training system developed by Hokoma, lifts a patient’s body to hold up part of the patient’s weight, and controls the speed of the subject’s passive walking based on his/her gait func‐ tional level by changing the treadmill speed. It is important in robot-aided rehabilitation to choose suitable motion and external force trajectories according to the symptomatic states and damaged segments of the patients. The proposed controller simultaneously realizes rehabilitation motion tracking and force generation with a limited joint number of the rehabilitation robot. Using simulations and experimental results, we show the usefulness of the proposed control method
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