Convolutional Neural Network for Hand Gesture Recognition using 8 different EMG Signals

Pinzón-Arenas, Javier Orlando; Moreno, Róbinson Jiménez; Herrera-Benavides, Julian Esteban

doi:10.1109/stsiva.2019.8730272

Cited by 21 publications

(13 citation statements)

References 8 publications

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“…Tsinganos [18] proposed a modified CNN and achieved an improvement of 3% on NinaPro dataset. Pinzón-Arenas [19] used CNN to recognize six hand gestures using a wearable EMG recording device (Myo Armband, Thalamic Labs) and achieved a validation accuracy of 98.4% and 99% testing accuracy.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Convolutional Neural Network for Hand Gesture Recognition Using EMG

Asif

Waris

Gilani

et al. 2020

Sensors

103

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Electromyography (EMG) is a measure of electrical activity generated by the contraction of muscles. Non-invasive surface EMG (sEMG)-based pattern recognition methods have shown the potential for upper limb prosthesis control. However, it is still insufficient for natural control. Recent advancements in deep learning have shown tremendous progress in biosignal processing. Multiple architectures have been proposed yielding high accuracies (>95%) for offline analysis, yet the delay caused due to optimization of the system remains a challenge for its real-time application. From this arises a need for optimized deep learning architecture based on fine-tuned hyper-parameters. Although the chance of achieving convergence is random, however, it is important to observe that the performance gain made is significant enough to justify extra computation. In this study, the convolutional neural network (CNN) was implemented to decode hand gestures from the sEMG data recorded from 18 subjects to investigate the effect of hyper-parameters on each hand gesture. Results showed that the learning rate set to either 0.0001 or 0.001 with 80-100 epochs significantly outperformed (p < 0.05) other considerations. In addition, it was observed that regardless of network configuration some motions (close hand, flex hand, extend the hand and fine grip) performed better (83.7% ± 13.5%, 71.2% ± 20.2%, 82.6% ± 13.9% and 74.6% ± 15%, respectively) throughout the course of study. So, a robust and stable myoelectric control can be designed on the basis of the best performing hand motions. With improved recognition and uniform gain in performance, the deep learning-based approach has the potential to be a more robust alternative to traditional machine learning algorithms.

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Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Convolutional Neural Network for Hand Gesture Recognition Using EMG

Asif

Waris

Gilani

et al. 2020

Sensors

103

View full text Add to dashboard Cite

show abstract

“…Moreover, our developed method has the advantage of using small training datasets. In Arteaga et al ( 2020 ) and Pinzón-Arenas et al ( 2019 ), each gesture was repeated for more than 10 times. Whereas, in our method beside calibration, rest data is recorded for only one time.…”

Section: Discussionmentioning

confidence: 99%

“…Proper control of the exoskeleton depends mainly on accurate human intention detection. Several methods to determine human intention that are based on electromyography (EMG) (Anam et al, 2017 ; Meng et al, 2017 ; Pinzón-Arenas et al, 2019 ; Qi et al, 2019 ; Zhang et al, 2019 ; Asif et al, 2020 ) and force myography (FMG) (Islam and Bai, 2019 ; Xiao and Menon, 2019 , 2020 ) have been proposed. Leonardis et al ( 2015 ) used EMG to control a hand exoskeleton for bilateral rehabilitation.…”

Section: Introductionmentioning

confidence: 99%

Effective Multi-Mode Grasping Assistance Control of a Soft Hand Exoskeleton Using Force Myography

Islam

Bai

2020

Front. Robot. AI

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Human intention detection is fundamental to the control of robotic devices in order to assist humans according to their needs. This paper presents a novel approach for detecting hand motion intention, i.e., rest, open, close, and grasp, and grasping force estimation using force myography (FMG). The output is further used to control a soft hand exoskeleton called an SEM Glove. In this method, two sensor bands constructed using force sensing resistor (FSR) sensors are utilized to detect hand motion states and muscle activities. Upon placing both bands on an arm, the sensors can measure normal forces caused by muscle contraction/relaxation. Afterwards, the sensor data is processed, and hand motions are identified through a threshold-based classification method. The developed method has been tested on human subjects for object-grasping tasks. The results show that the developed method can detect hand motions accurately and to provide assistance w.r.t to the task requirement.

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“…The signal threshold-crossing event number was exploited and then the average ATC classifier produced 92.87% accuracy. Arenas et al [19] collected data via eight Myo armband sensors with the use of a power spectral density map. For classification, they built a feature set consisting of 2880 multichannel feature maps, which were divided into three equal sets for training, validation, and testing.…”

Section: Literature Review 21 Hgr Through Electromyographic Signalsmentioning

confidence: 99%

“…where ε2 a1 and ε1 a1 change the value according to ( 17), (18), and (19). Otherwise, the local values will have constant values ε1 a1 = ε1 min and ε2 a1 = 0.…”

Section: Mesh Geometrymentioning

confidence: 99%

Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities

et al. 2021

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Due to the constantly increasing demand for the automatic localization of landmarks in hand gesture recognition, there is a need for a more sustainable, intelligent, and reliable system for hand gesture recognition. The main purpose of this study was to develop an accurate hand gesture recognition system that is capable of error-free auto-landmark localization of any gesture dateable in an RGB image. In this paper, we propose a system based on landmark extraction from RGB images regardless of the environment. The extraction of gestures is performed via two methods, namely, fused and directional image methods. The fused method produced greater extracted gesture recognition accuracy. In the proposed system, hand gesture recognition (HGR) is done via several different methods, namely, (1) HGR via point-based features, which consist of (i) distance features, (ii) angular features, and (iii) geometric features; (2) HGR via full hand features, which are composed of (i) SONG mesh geometry and (ii) active model. To optimize these features, we applied gray wolf optimization. After optimization, a reweighted genetic algorithm was used for classification and gesture recognition. Experimentation was performed on five challenging datasets: Sign Word, Dexter1, Dexter + Object, STB, and NYU. Experimental results proved that auto landmark localization with the proposed feature extraction technique is an efficient approach towards developing a robust HGR system. The classification results of the reweighted genetic algorithm were compared with Artificial Neural Network (ANN) and decision tree. The developed system plays a significant role in healthcare muscle exercise.

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Convolutional Neural Network for Hand Gesture Recognition using 8 different EMG Signals

Cited by 21 publications

References 8 publications

Performance Evaluation of Convolutional Neural Network for Hand Gesture Recognition Using EMG

Performance Evaluation of Convolutional Neural Network for Hand Gesture Recognition Using EMG

Effective Multi-Mode Grasping Assistance Control of a Soft Hand Exoskeleton Using Force Myography

Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities

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