Myoelectric Control Systems for Upper Limb Wearable Robotic Exoskeletons and Exosuits—A Systematic Review

Fu, Jirui; Choudhury, Renoa; Hosseini, Saba M.; Simpson, Rylan; Park, Joon‐Hyuk

doi:10.3390/s22218134

Cited by 23 publications

(14 citation statements)

References 112 publications

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“…The proposed method has also been investigated for rehabilitation purposes for both upper and lower limb exoskeletons, demonstrating its applicability in multiple settings [ 14 ]. Other studies have focused on the design and evaluation of surface electromyography-controlled lightweight upper arm exoskeleton rehabilitation robots, highlighting the potential benefits of utilizing fuzzy logic-based pain detection approaches in optimizing stimulation control systems in such robots [ 15 ]. To maximize the efficacy of upper limb rehabilitation robots, various strategies are being employed.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…Through the analysis of sEMG data, traditional ML algorithms such as support vector machines (SVMs) and linear discriminant analysis (LDA) have been employed to identify the intended hand 2 . Although conventional pattern-recognition-based myoelectric control has received much scholarly attention over the last few decades, cutting-edge approaches have not been applied in many practical commercial applications 3 . This is due, among many others, to the intrinsic data-driven nature of the ML and DNN algorithms.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the performance of convolutional neural network for enhanced gesture recognition using sEMG

Ashraf,

Waris,

Gilani

et al. 2024

Sci Rep

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Deep neural networks (DNNs) have demonstrated higher performance results when compared to traditional approaches for implementing robust myoelectric control (MEC) systems. However, the delay induced by optimising a MEC remains a concern for real-time applications. As a result, an optimised DNN architecture based on fine-tuned hyperparameters is required. This study investigates the optimal configuration of convolutional neural network (CNN)-based MEC by proposing an effective data segmentation technique and a generalised set of hyperparameters. Firstly, two segmentation strategies (disjoint and overlap) and various segment and overlap sizes were studied to optimise segmentation parameters. Secondly, to address the challenge of optimising the hyperparameters of a DNN-based MEC system, the problem has been abstracted as an optimisation problem, and Bayesian optimisation has been used to solve it. From 20 healthy people, ten surface electromyography (sEMG) grasping movements abstracted from daily life were chosen as the target gesture set. With an ideal segment size of 200 ms and an overlap size of 80%, the results show that the overlap segmentation technique outperforms the disjoint segmentation technique (p-value < 0.05). In comparison to manual (12.76 ± 4.66), grid (0.10 ± 0.03), and random (0.12 ± 0.05) search hyperparameters optimisation strategies, the proposed optimisation technique resulted in a mean classification error rate (CER) of 0.08 ± 0.03 across all subjects. In addition, a generalised CNN architecture with an optimal set of hyperparameters is proposed. When tested separately on all individuals, the single generalised CNN architecture produced an overall CER of 0.09 ± 0.03. This study's significance lies in its contribution to the field of EMG signal processing by demonstrating the superiority of the overlap segmentation technique, optimizing CNN hyperparameters through Bayesian optimization, and offering practical insights for improving prosthetic control and human–computer interfaces.

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“…Additionally, the sensor fusion technique [27] for combining EMG and IMU sensor data is known to improve the performance of ML predictions [28]. While it is commonly used for assistive devices of the upper [29] and lower limbs [30], [31], sensor fusion has not been applied to trunk assistive devices. Therefore, this study aimed to utilize the sensor fusion technique with data from EMG and IMU sensors for trunk movement intention detection with the chair-mounted passive trunk orthosis (CMPTO).…”

Section: Introductionmentioning

confidence: 99%

Sensor Fusion and Machine Learning for Seated Movement Detection With Trunk Orthosis

Zahid Rao,

Shahid Siddique,

Danish Mujib

et al. 2024

IEEE Access

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Advanced assistive devices developed for activities of daily living use machine learning (ML) for motion intention detection using wearable sensors. Trunk assistive devices provide safety, balance, and independence for wheelchair-bound individuals who spend prolonged hours in sitting positions. We used ML for trunk movement intention detection with a trunk orthosis. Sensor fusion technique with four electromyography (EMG) and one inertial measurement unit (IMU) sensor signals are used to develop a three-level classification system. Forty participants engaged in seated trunk movement trials wearing the orthosis. The trials comprised 30 movements involving trunk flexion/extension, lateral bending, and axial rotation. The wrapper method was used to reduce essential EMG features. Ensemble (ES), k-nearest neighbors (KNN), and support vector machine ML classifiers were used. Twenty-six features (five EMG for each of four muscles and six for IMU) were used to develop ten individual ML models, resulting in an average accuracy of 95.44%. Eight models achieved the highest accuracy with the ES and two with KNN. The models were then cascaded to form a trunk motion detection system that achieved a test accuracy of 87.0%. The promising result of this study can be implemented for trunk motion recognition with active trunk orthosis.

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Myoelectric Control Systems for Upper Limb Wearable Robotic Exoskeletons and Exosuits—A Systematic Review

Cited by 23 publications

References 112 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

Optimizing the performance of convolutional neural network for enhanced gesture recognition using sEMG

Sensor Fusion and Machine Learning for Seated Movement Detection With Trunk Orthosis

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