Surface EMG Pattern Recognition Using Long Short-Term Memory Combined with Multilayer Perceptron

He, Yong; Fukuda, Osamu; Bu, Nan; Okumura, Hiroshi; Yamaguchi, Nobuhiko

doi:10.1109/embc.2018.8513595

Cited by 51 publications

(39 citation statements)

References 10 publications

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“…It is proved that the dynamic models (RNN) with fewer parameters can achieve similar accuracy to the static model (FFNN), and the training and inference time is shorter. He et al ( 2018 ) propose a model combining long short-term memory (LSTM) network and multiplayer perceptron (MLP) for feature learning and classification of sEMG signals. The former captures the temporal dependence of sEMG signals, while the latter focuses on static characteristics.…”

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.

102

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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.

102

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“…Teban et al claimed that LSTM performed better than a non-recurrent ANN in replicating a non-linear mechanism of a real human hand [23]. He et al combined LSTM with ANN to exploit both the dynamic and static information of sEMG [24]. Ali et al validated that a bidirectional LSTM with attention mechanism could outperform other tested recurrent neural networks (RNN) in sEMG-based hand gesture recognition [25].…”

Section: Introductionmentioning

confidence: 99%

A CNN-LSTM Hybrid Model for Wrist Kinematics Estimation Using Surface Electromyography

Bao

Zaidi

Xie

et al. 2021

IEEE Trans. Instrum. Meas.

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Convolutional neural network (CNN) has been widely exploited for simultaneous and proportional myoelectric control due to its capability of deriving informative, representative and transferable features from surface electromyography (sEMG). However, muscle contractions have strong temporal dependencies but conventional CNN can only exploit spatial correlations. Considering that long short-term memory neural network (LSTM) is able to capture long-term and non-linear dynamics of time-series data, in this paper we propose a CNN-LSTM hybrid model to fully explore the temporal-spatial information in sEMG. Firstly, CNN is utilized to extract deep features from sEMG spectrum, then these features are processed via LSTM-based sequence regression to estimate wrist kinematics. Six healthy participants are recruited for the participatory collection and motion analysis under various experimental setups. Estimation results in both intra-session and inter-session evaluations illustrate that CNN-LSTM significantly outperforms CNN, LSTM and several representative machine learning approaches, particularly when complex wrist movements are activated. Index Terms-sEMG, wrist kinematics estimation, deep learning, convolutional neural network, long short-term memory network, hybrid model.

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“…Simao [ 28 ] compared the performance between forward propagation neural network and RNN on the recognition of eight hand gestures. He [ 29 ] combined long short-term memory (LSTM) and multiple layer perceptron to learn features of static hand gesture. Nadia [ 30 ] used raw sEMG signal to recognize six gestures, which could adapt to new subjects.…”

Section: Introductionmentioning

confidence: 99%

“…However, to our knowledge, most of the existing RNN-based methods [ 27 , 28 , 29 , 30 , 31 , 32 ] still focus on hand gesture recognition, which recognize hand gestures based on the whole temporal sEMG data, but make a prediction by RNN, which has already been used in other fields [ 33 , 34 , 35 , 36 ] and has not been studied. In this paper, we propose a novel method by using RNN to predict hand gesture.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Yang

2020

Sensors

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Surface electromyographic signal (sEMG) is a kind of bioelectrical signal, which records the data of muscle activity intensity. Most sEMG-based hand gesture recognition, which uses machine learning as the classifier, depends on feature extraction of sEMG data. Recently, a deep leaning-based approach such as recurrent neural network (RNN) has provided a choice to automatically learn features from raw data. This paper presents a novel hand gesture prediction method by using an RNN model to learn from raw sEMG data and predict gestures. The sEMG signals of 21 short-term hand gestures of 13 subjects were recorded with a Myo armband, which is a non-intrusive, low cost, commercial portable device. At the start of the gesture, the trained model outputs an instantaneous prediction for the sEMG data. Experimental results showed that the more time steps of data that were known, the higher instantaneous prediction accuracy the proposed model gave. The predicted accuracy reached about 89.6% when the data of 40-time steps (200 ms) were used to predict hand gesture. This means that the gesture could be predicted with a delay of 200 ms after the hand starts to perform the gesture, instead of waiting for the end of the gesture.

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Surface EMG Pattern Recognition Using Long Short-Term Memory Combined with Multilayer Perceptron

Cited by 51 publications

References 10 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

A CNN-LSTM Hybrid Model for Wrist Kinematics Estimation Using Surface Electromyography

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

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