An EMG-Based Biomimetic Variable Stiffness Modulation Strategy for Bilateral Motor Skills Relearning of Upper Limb Elbow Joint Rehabilitation

Yang, Ziyi; Guo, Shuxiang; Suzuki, Koji; Liu, Yi; Kawanishi, Masahiko

doi:10.1007/s42235-023-00339-9

Cited by 8 publications

(4 citation statements)

References 41 publications

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“…The subject experienced an approximate 83% increase in joint RoM compared to the initial state (see Table 3 ), validating the robot’s efficacy as a valuable tool for improving the range of motion, exhibiting competitive performance compared to recent similar works [ 26 , 27 , 31 , 32 ]. Moreover, the evaluation of estimated joint torque showcased a 30% reduction in passive torque, an aspect scarcely reported in recent research on human joint progress post-robotic rehabilitation training [ 33 , 34 , 35 , 36 ].…”

Section: Resultsmentioning

confidence: 99%

An Optimized Stimulation Control System for Upper Limb Exoskeleton Robot-Assisted Rehabilitation Using a Fuzzy Logic-Based Pain Detection Approach

Abdallah,

Bouteraa

2024

Sensors

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The utilization of robotic systems in upper limb rehabilitation has shown promising results in aiding individuals with motor impairments. This research introduces an innovative approach to enhance the efficiency and adaptability of upper limb exoskeleton robot-assisted rehabilitation through the development of an optimized stimulation control system (OSCS). The proposed OSCS integrates a fuzzy logic-based pain detection approach designed to accurately assess and respond to the patient’s pain threshold during rehabilitation sessions. By employing fuzzy logic algorithms, the system dynamically adjusts the stimulation levels and control parameters of the exoskeleton, ensuring personalized and optimized rehabilitation protocols. This research conducts comprehensive evaluations, including simulation studies and clinical trials, to validate the OSCS’s efficacy in improving rehabilitation outcomes while prioritizing patient comfort and safety. The findings demonstrate the potential of the OSCS to revolutionize upper limb exoskeleton-assisted rehabilitation by offering a customizable and adaptive framework tailored to individual patient needs, thereby advancing the field of robotic-assisted rehabilitation.

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Section: Resultsmentioning

confidence: 99%

An Optimized Stimulation Control System for Upper Limb Exoskeleton Robot-Assisted Rehabilitation Using a Fuzzy Logic-Based Pain Detection Approach

Abdallah,

Bouteraa

2024

Sensors

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“…Bilateral upper limb rehabilitation is a rehabilitation training strategy in which the healthy side drives the affected side to perform synchronous movements [ 4 ]. In a previous study by our research group, a gear-driven powered exoskeleton device was developed [ 8 , 9 , 10 ]. As shown in Figure 1 , the sEMG signals for different upper limb movements are collected.…”

Section: Methodsmentioning

confidence: 99%

“…sEMG signals can directly reflect the activation of superficial muscles with rich limp motion control information. In recent years, sEMG signal as the exoskeleton control signal source has been widely applied to a rehabilitation estimation [ 8 , 9 , 10 ], human intention prediction [ 11 , 12 , 13 , 14 , 15 , 16 , 17 ], and rehabilitation robot control [ 18 , 19 , 20 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Low-Density sEMG-Based Pattern Recognition of Unrelated Movements Rejection for Wrist Joint Rehabilitation

Guo

et al. 2023

Micromachines

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sEMG-based pattern recognition commonly assumes a limited number of target categories, and the classifiers often predict each target category depending on probability. In wrist rehabilitation training, the patients may make movements that do not belong to the target category unconsciously. However, most pattern recognition methods can only identify limited patterns and are prone to be disturbed by abnormal movement, especially for wrist joint movements. To address the above the problem, a sEMG-based rejection method for unrelated movements is proposed to identify wrist joint unrelated movements using center loss. In this paper, the sEMG signal collected by the Myo armband is used as the input of the sEMG control method. First, the sEMG signal is processed by sliding signal window and image coding. Then, the CNN with center loss and softmax loss is used to describe the spatial information from the sEMG image to extract discriminative features and target movement recognition. Finally, the deep spatial information is used to train the AE to reject unrelated movements based on the reconstruction loss. The results show that the proposed method can realize the target movements recognition and reject unrelated movements with an F-score of 93.4% and a rejection accuracy of 95% when the recall is 0.9, which reveals the effectiveness of the proposed method.

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“…The brain creates electrical impulses that are sent to muscle fibres through the spinal cord and nerve fibres to activate movements [6,7]. EMG-based research for medical assistance and smart gadgets, such as stroke assessment [8], analysis of neural impairments [9], heartbeat analysis [10], prosthetics [11], rehabilitation [12,13], physical training assessment [14,15], sports analytics [16], movement recognition and analysis [17], affective computing [18], human-machine interfaces [19,20], text input for the disabled [21], bio-signal fusion [22], and general healthcare [23], has been active. To aid rehabilitation from a distance, interactive forms for telerehabilitation can be used to measure the development of patients' range of motion (ROM) in real time using artificial intelligence algorithms, by manipulating the angles of action of limbs about a joint [24].…”

Section: Introductionmentioning

confidence: 99%

BiomacEMG: A Pareto-Optimized System for Assessing and Recognizing Hand Movement to Track Rehabilitation Progress

et al. 2023

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One of the most difficult components of stroke therapy is regaining hand mobility. This research describes a preliminary approach to robot-assisted hand motion therapy. Our objectives were twofold: First, we used machine learning approaches to determine and describe hand motion patterns in healthy people. Surface electrodes were used to collect electromyographic (EMG) data from the forearm’s flexion and extension muscles. The time and frequency characteristics were used as parameters in machine learning algorithms to recognize seven hand gestures and track rehabilitation progress. Eight EMG sensors were used to capture each contraction of the arm muscles during one of the seven actions. Feature selection was performed using the Pareto front. Our system was able to reconstruct the kinematics of hand/finger movement and simulate the behaviour of every motion pattern. Analysis has revealed that gesture categories substantially overlap in the feature space. The correlation of the computed joint trajectories based on EMG and the monitored hand movement was 0.96 on average. Moreover, statistical research conducted on various machine learning setups revealed a 92% accuracy in measuring the precision of finger motion patterns.

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An EMG-Based Biomimetic Variable Stiffness Modulation Strategy for Bilateral Motor Skills Relearning of Upper Limb Elbow Joint Rehabilitation

Cited by 8 publications

References 41 publications

An Optimized Stimulation Control System for Upper Limb Exoskeleton Robot-Assisted Rehabilitation Using a Fuzzy Logic-Based Pain Detection Approach

An Optimized Stimulation Control System for Upper Limb Exoskeleton Robot-Assisted Rehabilitation Using a Fuzzy Logic-Based Pain Detection Approach

Low-Density sEMG-Based Pattern Recognition of Unrelated Movements Rejection for Wrist Joint Rehabilitation

BiomacEMG: A Pareto-Optimized System for Assessing and Recognizing Hand Movement to Track Rehabilitation Progress

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