EMG-controlled hand exoskeleton for assisted bilateral rehabilitation

Cruz-Sánchez, Berith Atemoztli De la; Arias-Montiel, Manuel; Lugo-González, Esther

doi:10.1016/j.bbe.2022.04.001

Cited by 22 publications

(20 citation statements)

References 55 publications

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“…Researchers have developed hand exoskeletons using hard/rigid material rigid or flexible linkage for rehabilitation [4] , [5] , [6] , [7] and assistive devices [8] , [9] , [10] , [11] . Researchers developed 3D printed exoskeleton for lightweight, easy customization, and affordable manufacturing cost [12] , [13] , [14] , [15] .…”

Section: Hardware In Contextmentioning

confidence: 99%

Design and testing of fabric-based portable soft exoskeleton glove for hand grasping assistance in daily activity

Ismail,

Ariyanto,

Setiawan

et al. 2024

HardwareX

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Section: Hardware In Contextmentioning

confidence: 99%

Design and testing of fabric-based portable soft exoskeleton glove for hand grasping assistance in daily activity

Ismail,

Ariyanto,

Setiawan

et al. 2024

HardwareX

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“…There are different trends in EMG-Based robotic exoskeletons like increasing degree of freedom, decreasing weight, using soft material, and using different types of EMG sensors. In this section, current trends in the myoelectric activity control studies of the robotic exoskeletons such as elbow and wrist [ 45 ], arm [ 46 ], elbow joint [ 47 , 48 ], hand [ 61 , 62 , 63 , 64 ], elbow, wrist, and fingers [ 65 ], and wrist and hand [ 66 ] exoskeletons will be included.…”

Section: Emg-based Control System For Upper Limb Exoskeletonmentioning

confidence: 99%

“…It features 8-DOF, was created using 3D printing, and the fingers may move freely. Fuzzy logic was used to control the movement [ 64 ]. A brief description of each rehabilitation robot in Table II .…”

Section: Emg-based Control System For Upper Limb Exoskeletonmentioning

confidence: 99%

A review on EMG/EEG based control scheme of upper limb rehabilitation robots for stroke patients

Sarhan

Al-Faiz

Takhakh

2023

Heliyon

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“…This approach presents distinct advantages in assisting stroke victims' recovery by facilitating targeted rehabilitation (Park et al, 2020). The use of upper-limb prostheses (Fatimah et al, 2021;Amanpreet 2019;Gaudet et al, 2018) and rehabilitation exoskeletons (Gordleeva et al, 2020;De la Cruz-Sánchez et al, 2022) demonstrates the therapeutic significance of this technology, particularly for patients with limited mobility. In recent years, the availability of cost-effective sensor devices such as the Myo armband has spurred applied research in this domain, with high user compliance reported (Park et al, 2018).…”

Section: -Introductionmentioning

confidence: 99%

Hand gestures classification of sEMG signals based on BiLSTM- Metaheuristic Optimization and Hybrid U-Net-MobileNetV2 Encoder Architecture

Khavari,

Rezaee,

Ansari

et al. 2024

Preprint

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Surface electromyography (sEMG) data has been extensively utilized in deep learning algorithms for hand movement classification. This paper aims to introduce a novel method for hand gesture classification using sEMG data, addressing accuracy challenges seen in previous studies. We propose a U-Net architecture incorporating a MobileNetV2 encoder, enhanced by a novel Bidirectional Long Short-Term Memory (BiLSTM) and metaheuristic optimization for spatial feature extraction in hand gesture and motion recognition. Bayesian optimization is employed as the metaheuristic approach to optimize the BiLSTM model's architecture. To tackle the non-stationarity of sEMG signals, we utilize a windowing and overlapping method, augmented with additional signals in deep learning architectures. The MobileNetV2 encoder and U-Net architecture extract relevant features from sEMG spectrogram images. Edge computing integration is leveraged to further enhance innovation by enabling real-time processing and decision-making closer to the data source. Six standard databases were utilized, achieving an average accuracy of 90.23% with our proposed model, showcasing a 3-4% average accuracy improvement and a 10% variance reduction. Notably, Mendeley Data, BioPatRec DB3, and BioPatRec DB1 surpassed advanced models in their respective domains with classification accuracies of 88.71%, 90.2%, and 88.6%, respectively. Experimental results underscore the significant enhancement in generalizability and gesture recognition robustness. This approach offers a fresh perspective on prosthetic management and human-machine interaction, emphasizing its efficacy in improving accuracy and reducing variance for enhanced prosthetic control and interaction with machines through edge computing integration.

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EMG-controlled hand exoskeleton for assisted bilateral rehabilitation

Cited by 22 publications

References 55 publications

Design and testing of fabric-based portable soft exoskeleton glove for hand grasping assistance in daily activity

Design and testing of fabric-based portable soft exoskeleton glove for hand grasping assistance in daily activity

A review on EMG/EEG based control scheme of upper limb rehabilitation robots for stroke patients

Hand gestures classification of sEMG signals based on BiLSTM- Metaheuristic Optimization and Hybrid U-Net-MobileNetV2 Encoder Architecture

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