A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network

Zhang, Zhen; He, Changxin; Yang, Kuo

doi:10.3390/s20143994

Cited by 36 publications

(26 citation statements)

References 37 publications

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“…A threshold should be determined, and then all the instants greater than or equal to the threshold are extracted from the smoothed signal. The first of these instants is the beginning of the muscle activity region, and the last instant is the end of the activity region (Zhang et al, 2011 , 2020 ; Benalcázar et al, 2017 ; Qi et al, 2020 ). As depicted in Figure 3 , CH1-8 are eight channel sEMG signals, and S 2 is the standard deviation of eight channel sEMG signals calculated by moving average method.…”

Section: Pattern Recognition-based Semgmentioning

confidence: 99%

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

Shi

2021

Front. Neurosci.

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Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life, and limits their performance in activities of daily life. The realization of natural control for prosthetic hands is crucial to improving the quality of life of amputees. Surface electromyography (sEMG) signal is one of the most widely used biological signals for the prediction of upper limb motor intention, which is an essential element of the control systems of prosthetic hands. The conversion of sEMG signals into effective control signals often requires a lot of computational power and complex process. Existing commercial prosthetic hands can only provide natural control for very few active degrees of freedom. Deep learning (DL) has performed surprisingly well in the development of intelligent systems in recent years. The significant improvement of hardware equipment and the continuous emergence of large data sets of sEMG have also boosted the DL research in sEMG signal processing. DL can effectively improve the accuracy of sEMG pattern recognition and reduce the influence of interference factors. This paper analyzes the applicability and efficiency of DL in sEMG-based gesture recognition and reviews the key techniques of DL-based sEMG pattern recognition for the prosthetic hand, including signal acquisition, signal preprocessing, feature extraction, classification of patterns, post-processing, and performance evaluation. Finally, the current challenges and future prospects in clinical application of these techniques are outlined and discussed.

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Section: Pattern Recognition-based Semgmentioning

confidence: 99%

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

Shi

2021

Front. Neurosci.

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“…Table 10 shows the performance and characteristics of our proposed system and other studies using the Myo band as a sensor device [ 61 ]. Even though the performance metrics of these studies cannot be compared directly due to different experiment settings, they are helpful for qualitative comparisons.…”

Section: Discussionmentioning

confidence: 99%

Development of Sign Language Motion Recognition System for Hearing-Impaired People Using Electromyography Signal

Tateno

Liu

2020

Sensors

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Sign languages are developed around the world for hearing-impaired people to communicate with others who understand them. Different grammar and alphabets limit the usage of sign languages between different sign language users. Furthermore, training is required for hearing-intact people to communicate with them. Therefore, in this paper, a real-time motion recognition system based on an electromyography signal is proposed for recognizing actual American Sign Language (ASL) hand motions for helping hearing-impaired people communicate with others and training normal people to understand the sign languages. A bilinear model is applied to deal with the on electromyography (EMG) data for decreasing the individual difference among different people. A long short-term memory neural network is used in this paper as the classifier. Twenty sign language motions in the ASL library are selected for recognition in order to increase the practicability of the system. The results indicate that this system can recognize these twenty motions with high accuracy among twenty participants. Therefore, this system has the potential to be widely applied to help hearing-impaired people for daily communication and normal people to understand the sign languages.

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“…The power of the major vote strategy for hand gesture recognition has been verified in many studies [ 17 , 19 , 29 ]. According to these studies, the major vote scheme could evidently improve the recognition accuracy within a finite sample.…”

Section: Methodsmentioning

confidence: 97%

“…In addition to the handcrafted features, reseachers [ 25 , 26 , 27 , 28 , 29 ] tried to use sequences of sEMG signals as the input. Park [ 25 ] applied deep learning to sEMG-based hand gesture recognition using a convolutional neural network.…”

Section: Related Workmentioning

confidence: 99%

“…Further study [ 31 ] explained the ability of a convolutional neural network to combine data from two different modalities. Zhang [ 29 ] proposed a recurrent neural network-based approach that takes a window of sequential sEMG as the input, and the model could output predictions for the past sampling step in the window. However, the more data the window contains, the longer the prediction delay.…”

Section: Related Workmentioning

confidence: 99%

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Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method

Zhao

Wang

et al. 2021

Sensors

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In recent years, surface electromyography (sEMG)-based human–computer interaction has been developed to improve the quality of life for people. Gesture recognition based on the instantaneous values of sEMG has the advantages of accurate prediction and low latency. However, the low generalization ability of the hand gesture recognition method limits its application to new subjects and new hand gestures, and brings a heavy training burden. For this reason, based on a convolutional neural network, a transfer learning (TL) strategy for instantaneous gesture recognition is proposed to improve the generalization performance of the target network. CapgMyo and NinaPro DB1 are used to evaluate the validity of our proposed strategy. Compared with the non-transfer learning (non-TL) strategy, our proposed strategy improves the average accuracy of new subject and new gesture recognition by 18.7% and 8.74%, respectively, when up to three repeated gestures are employed. The TL strategy reduces the training time by a factor of three. Experiments verify the transferability of spatial features and the validity of the proposed strategy in improving the recognition accuracy of new subjects and new gestures, and reducing the training burden. The proposed TL strategy provides an effective way of improving the generalization ability of the gesture recognition system.

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A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network

Cited by 36 publications

References 37 publications

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

Development of Sign Language Motion Recognition System for Hearing-Impaired People Using Electromyography Signal

Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method

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