EMG-Based Hand Gesture Classification with Long Short-Term Memory Deep Recurrent Neural Networks

Jabbari, Milad; Khushaba, Rami N.; Nazarpour, Kianoush

doi:10.1109/embc44109.2020.9175279

Cited by 44 publications

(26 citation statements)

References 23 publications

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“…Deep learning can automatically extract the best feature set from sEMG signals. Many researchers have explored the application of deep learning in MHRI-based movement prediction methods (Allard et al, 2016 ; Cote-Allard et al, 2019 ; Jabbari et al, 2020 ). Allard et al proposed a multi-layer CNN gesture prediction model based on sEMG for robot guidance tasks (Allard et al, 2016 ).…”

Section: Related Workmentioning

confidence: 99%

“…It has succeeded in many challenging image classification tasks (Huang et al, 2017 ; Jeyaraj and Nadar, 2019 ), surpassing methods that rely on handcrafted features (Hinton et al, 2012 ; Huang et al, 2017 ). Although most research still relies on handcrafted features, many recent works have explored the application of deep learning in MHRI (Allard et al, 2016 ; Cote-Allard et al, 2019 ; Jabbari et al, 2020 ). This kind of MHRI mostly combines long short-term memory networks (LSTM) and CNNs simply, ignoring the difference in contribution and synergy of sEMG feature channels of different subjects under the same movement.…”

Section: Introductionmentioning

confidence: 99%

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MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram

et al. 2021

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The human–robot interface (HRI) based on biological signals can realize the natural interaction between human and robot. It has been widely used in exoskeleton robots recently to help predict the wearer's movement. Surface electromyography (sEMG)-based HRI has mature applications on the exoskeleton. However, the sEMG signals of paraplegic patients' lower limbs are weak, which means that most HRI based on lower limb sEMG signals cannot be applied to the exoskeleton. Few studies have explored the possibility of using upper limb sEMG signals to predict lower limb movement. In addition, most HRIs do not consider the contribution and synergy of sEMG signal channels. This paper proposes a human–exoskeleton interface based on upper limb sEMG signals to predict lower limb movements of paraplegic patients. The interface constructs an channel synergy-based network (MCSNet) to extract the contribution and synergy of different feature channels. An sEMG data acquisition experiment is designed to verify the effectiveness of MCSNet. The experimental results show that our method has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 94.51 and 80.75%, respectively. Furthermore, feature visualization and model ablation analysis show that the features extracted by MCSNet are physiologically interpretable.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram

et al. 2021

View full text Add to dashboard Cite

show abstract

“…It functions as a physiological measurement technique, retrieving and utilizing data regarding an individual's emotional, cognitive, or effectiveness condition. In what is known as passive BCI, the goal of brain signal usage has been expanded beyond commanding an item or providing a substitute for certain functions [11].…”

Section: User State Monitoringmentioning

confidence: 99%

“…These signals, known as electroencephalograms or EEGs for short, Electrode's measure voltage and communicate that information (dry or wet) positioned over a human's scalp. In addition to the standard non-invasive electroencephalography, there is many invasive options that can analyze activities in the brain by implant electrodes directly on the human's skull [11].…”

Section: A Mental Statementioning

confidence: 99%

Analysis of Electroencephalography Signals using Particle Swarm Optimization

Elbohy¹,

Abdelhamed²,

Mousa³

et al. 2021

IJACSA

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Brain computer interface devices monitor the brain signals and convert them into control commands in an attempt to imitate certain human cognitive functions. Numerous studies and applications have developed, because of the researchers' interest in systems in recent years. The capacity to categorize electroencephalograms is essential for building effective braincomputer interfaces. In this paper, three experiments were performed in order to categorize the brain signals with the goal of improving a model for EEG data analysis. An investigation is carried out to detect the characteristics derived from interactions across channels that may be more accurate than features that could be taken from individuals. Many machine learning techniques were applied such as; K-Nearest Neighbors, Long Short-Term memory and Decision Tree in this paper in order to detect and analyze the EEG signals from three different datasets to determine the best accuracy results using the particle swarm optimization algorithm that obviously minimized the dimension of the feature vector and improved the accuracy results.

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“…A CNN model can detect and locate human neurophysiological features appearing anywhere in a given segment of muscle-activity signal. Recurrent Neural Networks (RNNs) have also been used [13]- [17] for the control of prosthetic systems. An RNN model can capture the underlying temporal dynamics from sEMG signals since each hidden cell comprises the information from all previous hidden cells and the observation of the current timestamp.…”

Section: Introductionmentioning

confidence: 99%

Deep Heterogeneous Dilation of LSTM for Transient-phase Gesture Prediction through High-density Electromyography: Towards Application in Neurorobotics

Sun

Libby

et al. 2021

Preprint

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Deep networks have been recently proposed to estimate motor intention using conventional bipolar surface electromyography (sEMG) signals for myoelectric control of neurorobots. In this regard, deepnets are generally challenged by long training times (affecting the practicality and calibration), complex model architectures (affecting the predictability of the outcomes), a large number of trainable parameters (increasing the need for big data), and possibly overfitting. Capitalizing on our recent work on homogeneous temporal dilation in a Recurrent Neural Network (RNN) model, this paper proposes, for the first time, heterogeneous temporal dilation in an LSTM model and applies that to high-density surface electromyography (HD-sEMG), allowing for decoding dynamic temporal dependencies with tunable temporal foci. In this paper, a 128-channel HD-sEMG signal space is considered due to the potential for enhancing the spatiotemporal resolution of human-robot interfaces. Accordingly, this paper addresses a challenging motor intention decoding problem of neurorobots, namely, transient intention identification. The aforementioned problem only takes into account the dynamic and transient phase of gesture movements when the signals are not stabilized or plateaued, addressing which can significantly enhance the temporal resolution of human-robot interfaces. This would eventually enhance seamless real-time implementations. Additionally, this paper introduces the concept of “dilation foci” to modulate the modeling of temporal variation in transient phases. In this work a high number (i.e. 65) of gestures is included, which adds to the complexity and significance of the understudied problem. Our results show state-of-the-art performance for gesture prediction in terms of accuracy, training time, and model convergence.

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EMG-Based Hand Gesture Classification with Long Short-Term Memory Deep Recurrent Neural Networks

Cited by 44 publications

References 23 publications

MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram

MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram

Analysis of Electroencephalography Signals using Particle Swarm Optimization

Deep Heterogeneous Dilation of LSTM for Transient-phase Gesture Prediction through High-density Electromyography: Towards Application in Neurorobotics

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