Impedance Control of a Wrist Rehabilitation Robot Based on Autodidact Stiffness Learning

Dynamic control of an intrinsically compliant robot is paramount to ensuring safe and synergistic assistance to the patient. This paper presents an impedance controller for the rehabilitation of stroke patients with compromised wrist motor functions. The control design employs a Koopman operator-bas...

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Published inIEEE transactions on medical robotics and bionics Vol. 4; no. 3; pp. 796 - 806
Main Authors Goyal, Tanishka, Hussain, Shahid, Martinez-Marroquin, Elisa, Brown, Nicholas A. T., Jamwal, Prashant K.
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Actuators
Anatomical stiffness prediction
biomimetic muscle actuators (BMA)
Biomimetics
Controllers
Dynamic control
End effectors
Human motion
Impedance
impedance control
Joints (anatomy)
Koopman operator
Medical treatment
Muscles
non-linear control
Parallel robots
Rehabilitation robots
Robots
Stiffness
System identification
Wrist
wrist rehabilitation robot
Online AccessGet full text
ISSN2576-3202
2576-3202
DOI10.1109/TMRB.2022.3194528

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Abstract Dynamic control of an intrinsically compliant robot is paramount to ensuring safe and synergistic assistance to the patient. This paper presents an impedance controller for the rehabilitation of stroke patients with compromised wrist motor functions. The control design employs a Koopman operator-based autodidactic system identification model to predict the anatomical stiffness of the wrist joint during its various degrees of rotational motion. The proposed impedance controller, perceiving the level of the subjects' participation from their joint stiffness, can modify the applied force. The end-effector robot has a parallel structure that uses four biomimetic muscle actuators as parallel links between the end-effector and the base platform. The controller performance is corroborated by testing the end-effector robot with three healthy subjects.
AbstractList Dynamic control of an intrinsically compliant robot is paramount to ensuring safe and synergistic assistance to the patient. This paper presents an impedance controller for the rehabilitation of stroke patients with compromised wrist motor functions. The control design employs a Koopman operator-based autodidactic system identification model to predict the anatomical stiffness of the wrist joint during its various degrees of rotational motion. The proposed impedance controller, perceiving the level of the subjects' participation from their joint stiffness, can modify the applied force. The end-effector robot has a parallel structure that uses four biomimetic muscle actuators as parallel links between the end-effector and the base platform. The controller performance is corroborated by testing the end-effector robot with three healthy subjects.
Author Jamwal, Prashant K.
Martinez-Marroquin, Elisa
Brown, Nicholas A. T.
Hussain, Shahid
Goyal, Tanishka
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SubjectTerms Actuators
Anatomical stiffness prediction
biomimetic muscle actuators (BMA)
Biomimetics
Controllers
Dynamic control
End effectors
Human motion
Impedance
impedance control
Joints (anatomy)
Koopman operator
Medical treatment
Muscles
non-linear control
Parallel robots
Rehabilitation robots
Robots
Stiffness
System identification
Wrist
wrist rehabilitation robot
Title Impedance Control of a Wrist Rehabilitation Robot Based on Autodidact Stiffness Learning
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