Myoelectric pattern recognition of hand motions for stroke rehabilitation

Castiblanco, Jenny C.; Ortmann, Steffen; Mondragón, Iván F.; Alvarado-Rojas, Catalina; Jöbges, Michael; Colorado, Julián

doi:10.1016/j.bspc.2019.101737

Cited by 31 publications

(41 citation statements)

References 30 publications

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“…This classification method shows higher performance than other classification methods, such as support vector machine and linear discriminant analysis, in the previous study. 40 In this study, the recognition accuracy of this method reaches an 86.25% correct classification rate. Different from the recognition of actions in the previous study, it is found through the assessment experiment that the classification accuracy presents a specific difference under training loads, and this may cause a decrease in the accuracy of dynamic recognition (about 6%) in the evaluation stage.…”

Section: Discussionmentioning

confidence: 60%

Design and evaluation of a surface electromyography-controlled lightweight upper arm exoskeleton rehabilitation robot

Liu

Zhu

et al. 2021

International Journal of Advanced Robotic Systems

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Nowadays, the rehabilitation robot has been developed for rehabilitation therapy. However, there are few studies on upper arm exoskeletons for rehabilitation training of muscle strength. This article aims to design a surface electromyography-controlled lightweight exoskeleton rehabilitation robot for home-based progressive resistance training. The exoskeleton’s lightweight structure is designed based on the kinematic model of the elbow joint and ergonomics sizes of the arm. At the same time, the overall weight of the exoskeleton is controlled at only 3.03 kg. According to the rehabilitation training task, we use torque limit mode to ensure stable torque output at variable velocity. We also propose a surface electromyography-based control method, which uses k- Nearest Neighbor algorithm to classify surface electromyographic signals under progressive training loads, and utilizes principal component analysis to improve the recognition accuracy to control the exoskeleton to provide muscle strength compensation. The assessment experiment of the exoskeleton rehabilitation robot shows that the dynamic recognition accuracy of this control method is 80.21%. Muscle activity of biceps brachii and triceps brachii under each training load decreases significantly when subjects with the exoskeleton robot. The results indicate that the exoskeleton rehabilitation robot can output the corresponding torque to assist in progressive resistance training. This study provides a solution to potential problems in the family-oriented application of exoskeleton rehabilitation robots.

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Section: Discussionmentioning

confidence: 60%

Design and evaluation of a surface electromyography-controlled lightweight upper arm exoskeleton rehabilitation robot

Liu

Zhu

et al. 2021

International Journal of Advanced Robotic Systems

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“…Many robotics-based systems provide bilateral grasp rehabilitation, which use muscle EMG of the unaffected hand and forearm for classification and trigger the execution to drive the affected limbs of the patients for rehabilitation training [33], [34]. Other studies have focused on intention detection based on the EMG signals from the affected side of stroke patients [35]- [37]. However, the sEMGbased classification results of movement intentions of stroke survivors are less accurate than those of healthy people due to neural damage.…”

Section: A Rehabilitationmentioning

confidence: 99%

“…Forearm sEMG-based intention detection has also been employed to control assistive exoskeletons or hand prostheses [43]. Most of the movements were selected from clinical-based assessment scales, and are highly related to ADLs, including wrist flexion, wrist extension, mass flexion, mass extension, hook-like grasp, opposition (hand pinch) and thumb adduction (lateral hand pinch), cylinder grip, and spherical grip [35]- [37], [44].…”

Section: B Prosthesis Controlmentioning

confidence: 99%

Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey

Jiang

Kang

Song

et al. 2022

IEEE Rev. Biomed. Eng.

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Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication. The purpose of this review was to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control, sign language recognition, and human-computer interaction. Results showed that electrical, dynamic, acoustical/vibratory, and optical sensing were the primary input modalities in gesture recognition interfaces. Two categories of algorithms were identified: 1) classification algorithms for predefined, fixed hand poses and 2) regression algorithms for continuous finger and wrist joint angles. Conventional machine learning algorithms, including linear discriminant analysis, support vector machines, random forests, and non-negative matrix factorization, have been widely used for a variety of gesture recognition applications, and deep learning algorithms have more recently been applied to further facilitate the complex relationship between sensor signals and multi-articulated hand postures. Future research should focus on increasing recognition accuracy with larger hand gesture datasets, improving reliability and robustness for daily use outside of the laboratory, and developing softer, less obtrusive interfaces.

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“…In previous work [ 26 , 27 , 28 ], we conducted significant studies to characterize muscle effort by extracting relevant features from the patient’s EMG signals and training machine learning models to identify hand motions according to standard clinical exercises applied for hand rehabilitation. Here, our contribution is twofold: (i) a combined model based on artificial neural networks (ANNs) and fuzzy logic was implemented to determine specific levels of actuation velocities for the exoskeleton, by identifying muscle effort from the EMG signals; (ii) an adaptive control scheme for the exoskeleton with feedback from the ANN-fuzzy model to actively counterbalance muscle effort in real-time.…”

Section: Introductionmentioning

confidence: 99%

Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection

Castiblanco

Mondragón

Alvarado-Rojas

et al. 2021

Sensors

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Robotic-assisted systems have gained significant traction in post-stroke therapies to support rehabilitation, since these systems can provide high-intensity and high-frequency treatment while allowing accurate motion-control over the patient’s progress. In this paper, we tackle how to provide active support through a robotic-assisted exoskeleton by developing a novel closed-loop architecture that continually measures electromyographic signals (EMG), in order to adjust the assistance given by the exoskeleton. We used EMG signals acquired from four patients with post-stroke hand impairments for training machine learning models used to characterize muscle effort by classifying three muscular condition levels based on contraction strength, co-activation, and muscular activation measurements. The proposed closed-loop system takes into account the EMG muscle effort to modulate the exoskeleton velocity during the rehabilitation therapy. Experimental results indicate the maximum variation on velocity was 0.7 mm/s, while the proposed control system effectively modulated the movements of the exoskeleton based on the EMG readings, keeping a reference tracking error <5%.

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Myoelectric pattern recognition of hand motions for stroke rehabilitation

Cited by 31 publications

References 30 publications

Design and evaluation of a surface electromyography-controlled lightweight upper arm exoskeleton rehabilitation robot

Design and evaluation of a surface electromyography-controlled lightweight upper arm exoskeleton rehabilitation robot

Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey

Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection

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