EMG-Based Model Predictive Control for Physical Human–Robot Interaction: Application for Assist-As-Needed Control

In this letter, we propose an electromyography (EMG)-based optimal control framework to design physical human-robot interaction for rehabilitation and develop a novel assist-as-needed (AAN) controller based on a model predictive control (MPC) approach. To enhance the recovery of motor functions, enc...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 3; no. 1; pp. 210 - 217
Main Authors: Teramae, Tatsuya, Noda, Tomoyuki, Morimoto, Jun
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive control
Elbow (anatomy)
Electromyography
Human engineering
Human motion
Optimal control
Physical human-robot interaction
physically assistive devices
Predictive control
Rehabilitation
Robot control
Robots
robust/adaptive control of robotic systems
Target tracking
Torque
Trajectory
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary:In this letter, we propose an electromyography (EMG)-based optimal control framework to design physical human-robot interaction for rehabilitation and develop a novel assist-as-needed (AAN) controller based on a model predictive control (MPC) approach. To enhance the recovery of motor functions, encouraging the voluntary movements of patients is necessary while a therapist is assisting them. Therefore, in an AAN control framework, the robot only assists the deficient torque to generate a target movement. In our study, we first estimate the joint torque of a patient from measured EMG signals and then derive the deficient joint torque to generate the target movements by considering the patient's estimated joint torque with an MPC method. Results showed that our proposed method adaptively derived the necessary torque to follow the target elbow joint trajectories based on the subject's voluntary movements.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2017.2737478