Review of Control Methods for Upper Limb Telerehabilitation With Robotic Exoskeletons

Bauer, Georgeta; Pan, Ya‐Jun

doi:10.1109/access.2020.3036596

Cited by 13 publications

(8 citation statements)

References 110 publications

(159 reference statements)

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“…TR enables healthcare professionals to deliver therapy remotely, potentially expanding access to underdeveloped areas. In [4], a recent review of control methods for upper limb telerehabilitation with robotic exoskeletons is presented. Moreover, several works present a new telerehabilitation platform with robotic devices for the at-home rehabilitation context [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A New Wrist–Forearm Rehabilitation Protocol Integrating Human Biomechanics and SVM-Based Machine Learning for Muscle Fatigue Estimation

2023

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In this research, a new remote rehabilitation system was developed that integrates an IoT-based connected robot intended for wrist and forearm rehabilitation. In fact, the mathematical model of the wrist and forearm joints was developed and integrated into the main controller. The proposed new rehabilitation protocol consists of three main sessions: the first is dedicated to the extraction of the passive components of the dynamic model of wrist–forearm biomechanics while the active components are extracted in the second session. The third session consists of performing continuous exercises using the determined dynamic model of the forearm–wrist joints, taking into account the torque generated by muscle fatigue. The main objective of this protocol is to determine the state level of the affected wrist and above all to provide a dynamic model in which the torque generated by the robot and the torque supplied by the patient are combined, taking into account the constraints of fatigue. A Support Vector Machine (SVM) classifier is designed for the estimation of muscle fatigue based on the features extracted from the electromyography (EMG) signal acquired from the patient. The results show that the developed rehabilitation system allows a good progression of the joint’s range of motion as well as the resistive-active torques.

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Section: Introductionmentioning

confidence: 99%

A New Wrist–Forearm Rehabilitation Protocol Integrating Human Biomechanics and SVM-Based Machine Learning for Muscle Fatigue Estimation

2023

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“…Articular torques are described by (1) and (2). The shoulder cable angle α s is easily obtained by solving the triangle given by the points O s BD according to (3) to (5).…”

Section: ) Torque Transmissionmentioning

confidence: 99%

“…There exist several control methods for upper-limb exoskeletons, as summarized in the review conducted in [5]. The use of these controllers depends on the exoskeleton application.…”

Section: Introductionmentioning

confidence: 99%

Robust Motion Control Architecture for an Upper-Limb Rehabilitation Exosuit

Pont-Esteban¹,

Sánchez-Urán²,

Ferré³

2022

IEEE Access

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Upper-limb exosuits are light wearable devices suitable for applications such as rehabilitation or assistance in industrial environments. One of the main drawbacks of these systems is the amount of uncertainties intrinsic to the nature of the device. Moreover, the direct interaction between the exosuit and its user adds disturbances to the system. Therefore, a robust control architecture to these phenomena is required for its appropriate control. In this article, a robust motion (position and speed) control architecture based on a cascade proportional-integral (PI) and a sliding mode controller (SMC), and a nonlinear disturbance observer (NDO) for the upper-limb cable-driven rehabilitation exosuit LUXBIT is designed. We call the proposed controller by the acronym CPISDO. The SMC nature allows to compensate the matched disturbances and uncertainties, while the NDO counteracts the unmatched ones. The PI component helps in reducing tracking error. Dynamic modelling, system architecture and control design are addressed. The CPISDO controller has been implemented over the exosuit and several experiments have been performed over 10 healthy subjects to evaluate the controller's tracking performance as well as the disturbance rejection. Supertwisting (a state-of-the-art SMC) and PI controllers have been implemented for CPISDO validation. Results are compared with other state-of-the-art solutions.INDEX TERMS Motion control, exoskeletons, control architectures, rehabilitation robotics, sliding-mode control.

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“…During conventional treatments, patients’ limb movements are carried out manually by the therapists. During the last two decades the use of robotic devices for upper limb physical therapy of stroke survivors [5] , [6] , [7] , [8] , [9] , [10] has gained popularity in rehabilitation clinics around the globe [11] , [12] , [13] , [14] , [15] . Several robotic systems have been developed and clinically evaluated with stroke survivors for upper limb rehabilitation [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Utilizing the intelligence edge framework for robotic upper limb rehabilitation in home

Jamwal¹,

Niyetkaliyev²,

Hussain³

et al. 2023

MethodsX

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Review of Control Methods for Upper Limb Telerehabilitation With Robotic Exoskeletons

Cited by 13 publications

References 110 publications

A New Wrist–Forearm Rehabilitation Protocol Integrating Human Biomechanics and SVM-Based Machine Learning for Muscle Fatigue Estimation

A New Wrist–Forearm Rehabilitation Protocol Integrating Human Biomechanics and SVM-Based Machine Learning for Muscle Fatigue Estimation

Robust Motion Control Architecture for an Upper-Limb Rehabilitation Exosuit

Utilizing the intelligence edge framework for robotic upper limb rehabilitation in home

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