Design and control of an exoskeleton robot with EMG-driven electrical stimulation for upper limb rehabilitation

Bouteraa, Yassine; Abdallah, Ismail Ben; Elmogy, Ahmed M.

doi:10.1108/ir-02-2020-0041

Cited by 33 publications

(29 citation statements)

References 40 publications

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“…In addition, based on the obtained EMG rows, a fuzzy classifier has been used to evaluate muscle exhaustion. Actually, due to its ability to model uncertainty and ambiguous input, fuzzy control has been widely used in many robotic applications [15,16]. In most fuzzy robotic applications, sensors are used to obtain information from uncertain environments to be used as inputs for the fuzzy controller which controls the behavior of the robot through the use of linguistic descriptions modeled using a rules database.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of a Smart IoT-Based Robotic Solution for Wrist Rehabilitation

et al. 2022

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In this study, we present an IoT-based robot for wrist rehabilitation with a new protocol for determining the state of injured muscles as well as providing dynamic model parameters. In this model, the torque produced by the robot and the torque provided by the patient are determined and updated taking into consideration the constraints of fatigue. Indeed, in the proposed control architecture based on the EMG signal extraction, a fuzzy classifier was designed and implemented to estimate muscle fatigue. Based on this estimation, the patient’s torque is updated during the rehabilitation session. The first step of this protocol consists of calculating the subject-related parameters. This concerns axis offset, inertial parameters, passive stiffness, and passive damping. The second step is to determine the remaining component of the wrist model, including the interaction torque. The subject must perform the desired movements providing the torque necessary to move the robot in the desired direction. In this case, the robot applies a resistive torque to calculate the torque produced by the patient. After that, the protocol considers the patient and the robot as active and all exercises are performed accordingly. The developed robotics-based solution, including the proposed protocol, was tested on three subjects and showed promising results.

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Section: Introductionmentioning

confidence: 99%

Design and Development of a Smart IoT-Based Robotic Solution for Wrist Rehabilitation

et al. 2022

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“…For KFE, six electrodes are attached to the muscles, including RF, vastus medialis (VM), vastus lateralis (VL), BF, SM and semitendinosus (ST) (Schutte et al , 1993). The raw EMG signals are sampled at 1,024 Hz, bandpass filtered from 10 Hz to 500 Hz and then notch filtered at 50 Hz to remove noise (Bouteraa et al , 2020). Four rehabilitation robots work in parallel using the asynchronous DDPG algorithm.…”

Section: Methodsmentioning

confidence: 99%

A robotic system with reinforcement learning for lower extremity hemiparesis rehabilitation

Cheng

et al. 2021

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Purpose This paper aims to propose a bilateral robotic system for lower extremity hemiparesis rehabilitation. The hemiplegic patients can complete rehabilitation exercise voluntarily with the assistance of the robot. The reinforcement learning is included in the robot control system, enhancing the muscle activation of the impaired limbs (ILs) efficiently with ensuring the patients’ safety. Design/methodology/approach A bilateral leader–follower robotic system is constructed for lower extremity hemiparesis rehabilitation, where the leader robot interacts with the healthy limb (HL) and the follow robot is worn by the IL. The therapeutic training is transferred from the HL to the IL with the assistance of the robot, and the IL follows the motion trajectory prescribed by the HL, which is called the mirror therapy. The model reference adaptive impedance control is used for the leader robot, and the reinforcement learning controller is designed for the follower robot. The reinforcement learning aims to increase the muscle activation of the IL and ensure that its motion can be mastered by the HL for safety. An asynchronous algorithm is designed by improving experience relay to run in parallel on multiple robotic platforms to reduce learning time. Findings Through clinical tests, the lower extremity hemiplegic patients can rehabilitate with high efficiency using the robotic system. Also, the proposed scheme outperforms other state-of-the-art methods in tracking performance, muscle activation, learning efficiency and rehabilitation efficacy. Originality/value Using the aimed robotic system, the lower extremity hemiplegic patients with different movement abilities can obtain better rehabilitation efficacy.

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“…In addition, hybrid time-frequency features are proposed to overcome the limitation of time features, which relies in stationary properties of the EMG signal. These features are less applied due to computation costs, and are on time-frequency methods such as Discrete Wavelet Transform and Wavelet Packet Transform (Phinyomark et al, 2012;Nazmi et al, 2016) Comment on current/potential applications: Currently, EMG features are being used to the enhancement of robot-assisted upper limb rehabilitation platforms, by means of using the subject's intentions to generate proper feedback for the robotic system (Cahyadi et al, 2018b;Bouteraa et al, 2020;Khairuddin et al, 2021). In particular, due to their relative low computational cost, their potential combination with machine learning algorithms and other technologies such as virtual reality (Meng et al, 2019) could be the key to develop dynamic rehabilitation devices that can boost the personalization of motor training (Abdallah et al, 2017;Arteaga et al, 2020;Samuel et al, 2021) 4.…”

Section: Emg Time and Frequency Domain Featuresmentioning

confidence: 99%

Neuromechanical Biomarkers for Robotic Neurorehabilitation

et al. 2021

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One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the “biomarkers” that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the “Rehabilomics” has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.

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Design and control of an exoskeleton robot with EMG-driven electrical stimulation for upper limb rehabilitation

Cited by 33 publications

References 40 publications

Design and Development of a Smart IoT-Based Robotic Solution for Wrist Rehabilitation

Design and Development of a Smart IoT-Based Robotic Solution for Wrist Rehabilitation

A robotic system with reinforcement learning for lower extremity hemiparesis rehabilitation

Neuromechanical Biomarkers for Robotic Neurorehabilitation

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