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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Vydané v:	IEEE robotics and automation letters Ročník 3; číslo 1; s. 210 - 217
Hlavní autori:	Teramae, Tatsuya, Noda, Tomoyuki, Morimoto, Jun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.01.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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Abstract	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.
AbstractList	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.
Author	Noda, Tomoyuki Morimoto, Jun Teramae, Tatsuya
Author_xml	– sequence: 1 givenname: Tatsuya surname: Teramae fullname: Teramae, Tatsuya email: t-teramae@atr.jp organization: Dept. of Brain Robot Interface, ATR Comput. Neurosci. Labs., Kyoto, Japan – sequence: 2 givenname: Tomoyuki surname: Noda fullname: Noda, Tomoyuki email: tomoyuki.noda@gmail.com organization: Dept. of Brain Robot Interface, ATR Comput. Neurosci. Labs., Kyoto, Japan – sequence: 3 givenname: Jun surname: Morimoto fullname: Morimoto, Jun email: xmorimo@atr.jp organization: Dept. of Brain Robot Interface, ATR Comput. Neurosci. Labs., Kyoto, Japan
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SubjectTerms	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
Title	EMG-Based Model Predictive Control for Physical Human–Robot Interaction: Application for Assist-As-Needed Control
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