EMG-based Hand Gesture Research Articles

Enhanced EMG-Based Hand Gesture Classification in Real-World Scenarios: Mitigating Dynamic Factors With Tempo-Spatial Wavelet Transform and Deep Learning

Enhanced EMG-Based Hand Gesture Classification in Real-World Scenarios: Mitigating Dynamic Factors With Tempo-Spatial Wavelet Transform and Deep Learning

Wavelet Transform as Pre-processing for EMG-based Hand Gesture Recognition Suitable for Actual User-environment

Wavelet Transform as Pre-processing for EMG-based Hand Gesture Recognition Suitable for Actual User-environment

CNN-LSTM and post-processing for EMG-based hand gesture recognition

Hand Gesture Recognition (HGR) using electromyography (EMG) signals is a challenging problem due to the variability and noise in the signals across individuals. This study addresses this challenge by examining the effect of incorporating a post-processing algorithm, which filters the sequence of predictions and removes spurious labels, on the performance of a HGR model based on spectrograms and Convolutional Neural Networks (CNN). The study also compares CNN vs CNN-LSTM to assess the influence of the memory cells on the model. The EMG-EPN-612 dataset, which contains measurements of EMG signals for 5 hand gestures from 612 subjects, was used for training and testing. The results showed that the post-processing algorithm increased the recognition accuracy by 41.86% for the CNN model and 24.77% for the CNN-LSTM model. The inclusion of the memory cells increased accuracy by 3.29%, but at the cost of 53 times more learnables. The CNN-LSTM model with post-processing achieved a mean recognition accuracy of 90.55% (SD=9.45%). These findings suggest new paths for research in HGR architectures beyond the traditional focus on the classification and feature extraction stages. For reproducibility purposes, we made publicly available the source code in Github.

EMG-based hand gesture classifier robust to daily variation: Recursive domain adversarial neural network with data synthesis

ObjectiveIf a supervised classification model is used to predict hand gestures using electromyography (EMG), the EMG signals for training should be labeled every day due to their daily variations. However, annotating these signals every day is time-consuming. MethodsTo address this problem, this study proposes a new framework that updates the EMG classifier in a semi-supervised manner; the classifier was optimized to a target day by using the labeled past EMG signals and the unlabeled signals of the target day. Specifically, the following models were integrated to maximize the classification accuracy: first, a domain-adversarial neural network (DANN) was used to account for the domain shift between the EMG signals of the past and target days. Second, the past dataset was augmented by a data synthesis model incorporated with clustering, random selection, and correlation (CRC). Lastly, a recursive DANN structure was developed to augment the unlabeled EMG signals of the target day. ResultsThe performance of the proposed framework was validated with EMG data of four subjects and five different days. The classification accuracies are 58.22% (without DANN), 66.65% (with DANN), and 73.67% (with recursive DANN and CRC), respectively. ConclusionsThe recursive DANN with CRC enhances the domain adaptation effect, and thus it can address the daily variation problem of EMG. SignificanceThe proposed framework can also be extended to resolve a subject variation problem of EMG.

An Ultra-Low Power Reconfigurable Biomedical AI Processor With Adaptive Learning for Versatile Wearable Intelligent Health Monitoring.

Wearable intelligent health monitoring devices with on-device biomedical AI processor can be used to detect the abnormity in users' biomedical signals (e.g., ECG arrythmia classification, EEG-based seizure detection). This requires ultra-low power and reconfigurable biomedical AI processor to support battery-supplied wearable devices and versatile intelligent health monitoring applications while achieving high classification accuracy. However, existing designs have issues in meeting one or more of the above requirements. In this work, a reconfigurable biomedical AI processor (named BioAIP) is proposed, mainly featuring: 1) a reconfigurable biomedical AI processing architecture to support versatile biomedical AI processing. 2) an event-driven biomedical AI processing architecture with approximate data compression to reduce the power consumption. 3) an AI-based adaptive-learning architecture to address patient-to-patient variation and improve the classification accuracy. The design has been implemented and fabricated using a 65nm CMOS process technology. It has been demonstrated with three typical biomedical AI applications, including ECG arrythmia classification, EEG-based seizure detection and EMG-based hand gesture recognition. Compared with the state-of-the-art designs optimized for single biomedical AI tasks, the BioAIP achieves the lowest energy per classification among the designs with similar accuracy, while supporting various biomedical AI tasks.

Deep Scattering Transform with Attention Mechanisms Improves EMG-based Hand Gesture Recognition.

Electromyogram (EMG) signals provide valuable insights into the muscles' activities supporting the different hand movements, but their analysis can be challenging due to their stochastic nature, noise, and non-stationary variations in the signal. We are pioneering the use of a unique combination of wavelet scattering transform (WST) and attention mechanisms adopted from recent sequence modelling developments of deep neural networks for the classification of EMG patterns. Our approach utilizes WST, which decomposes the signal into different frequency components, and then applies a non-linear operation to the wavelet coefficients to create a more robust representation of the extracted features. This is coupled with different variations of attention mechanisms, typically employed to focus on the most important parts of the input data by considering weighted combinations of all input vectors. By applying this technique to EMG signals, we hypothesized that improvement in the classification accuracy could be achieved by focusing on the correlation between the different muscles' activation states associated with the different hand movements. To validate the proposed hypothesis, the study was conducted using three commonly used EMG datasets collected from various environments based on laboratory and wearable devices. This approach shows significant improvement in myoelectric pattern recognition (PR) compared to other methods, with average accuracies of up to 98%.

A comparison of EMG-based hand gesture recognition systems based on supervised and reinforcement learning

Hand gesture recognition (HGR) based on electromyography signals (EMGs) has been one of the most relevant research topics in the human–machine interfaces field in recent years. The HGR systems are aimed at identifying the moment in which a hand gesture was performed as well as the gesture category. To date, several HGR state-of-the-art methods are based mainly on supervised machine learning (ML) techniques. However, the use of reinforcement learning (RL) approaches to classify EMGs has not yet been thoroughly evaluated. Moreover, the behavior of HGR systems based on ML and RL methods on large datasets for user-general HGR systems is still an open research problem. In the present work, we compare a supervised learning with a reinforcement learning HGR system, which are composed of the following stages: pre-processing, feature extraction, classification, and post-processing. We compared the performance of using both a supervised and a reinforcement learning method to classify and recognize EMGs for six different hand gestures. We performed experiments by using training, validation, and test sets on the EMG-EPN-612 public dataset, and the results were evaluated for user-general HGR models. The final accuracy results on the test set demonstrate that the best model was obtained for the supervised learning method, reaching up to 90.49%±9.7% and 86.83%±11.30% for classification and recognition accuracy respectively. The results obtained in this work demonstrated that supervised learning methods outperform reinforcement learning methods for user-general HGR systems based on EMGs for the EMG-EPN-612 dataset distribution.

A novel neuroevolution model for emg-based hand gesture classification

A novel neuroevolution model for emg-based hand gesture classification

LSTM-MSA: A Novel Deep Learning Model With Dual-Stage Attention Mechanisms Forearm EMG-Based Hand Gesture Recognition.

This paper introduces the Long Short-Term Memory with Dual-Stage Attention (LSTM-MSA) model, an approach for analyzing electromyography (EMG) signals. EMG signals are crucial in applications like prosthetic control, rehabilitation, and human-computer interaction, but they come with inherent challenges such as non-stationarity and noise. The LSTM-MSA model addresses these challenges by combining LSTM layers with attention mechanisms to effectively capture relevant signal features and accurately predict intended actions. Notable features of this model include dual-stage attention, end-to-end feature extraction and classification integration, and personalized training. Extensive evaluations across diverse datasets consistently demonstrate the LSTM-MSA's superiority in terms of F1 score, accuracy, recall, and precision. This research provides a model for real-world EMG signal applications, offering improved accuracy, robustness, and adaptability.

Recognition of Hand Gesture Using Electromyography Signal: Human-Robot Interaction

Recognition of hand gestures has been developed in various research domains and proven to have significant benefits in improving the necessity of human-robot interaction (HRI). The introduction of intelligent statistics knowledge methodologies, such as big data and machine learning, has ushered in a new era of data science and made it easier to classify hand motions accurately using electromyography (EMG) signals. However, the collecting and labelling of the vast dataset enforces a significant workload; resulting in implementations takes a long time. As a result, a unique strategy for combining the advantages of depth vision learning with EMG-based hand gesture detection was developed. It is accomplished of automatically categorizing the class of the obtained EMG data using ensemble learning without considering the hand motion sequence. The models were built and interpreted using the SVM with RBF kernel, Random Forest, and Catboost with the best hyperparameters. The resultant value states that Catboost produces the best accuracy of around 0.95 as compared with other models. This demonstrates that the suggested technique can recognize hand gestures with better performance rate.

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

Electromyogram (EMG) pattern recognition has been utilized with the traditional machine and deep learning architectures as a control strategy for upper-limb prostheses. However, most of these learning architectures, including those in convolutional neural networks, focus the spatial correlations only; but muscle contractions have a strong temporal dependency. Our primary aim in this paper is to investigate the effectiveness of recurrent deep learning networks in EMG classification as they can learn long-term and non-linear dynamics of time series. We used a Long Short-Term Memory (LSTM-based) neural network to perform multiclass classification with six grip gestures at three different force levels (low, medium, and high) generated by nine amputees. Four different feature sets were extracted from the raw signals and fed to LSTM. Moreover, to investigate a generalization of the proposed method, three different training approaches were tested including 1) training the network with feature extracted from one specific force level and testing it with the same force level, 2) training the network with one specific force level and testing it with two remained force levels, and 3) training the network with all of the force levels and testing it with a single force level. Our results show that LSTM-based neural network can provide reliable performance with average classification errors of around 9% across all nine amputees and force levels. We demonstrate the applicability of deep learning for upperlimb prosthesis control.

Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach.

Understanding and classifying electromyogram (EMG) signals is of significance for dexterous prosthetic hand control, sign languages, grasp recognition, human-machine interaction, etc.. The existing research of EMG-based hand gesture classification faces the challenges of unsatisfied classification accuracy, insufficient generalization ability, lack of training data and weak robustness. To address these problems, this paper combines unsupervised and supervised learning methods to classify an EMG dataset consisting of 10 classes of hand gestures. To lessen the difficulty of classification, clustering methods including subtractive clustering and fuzzy c-means (FCM) clustering algorithms are employed first to obtain the initial partition of the inputs. In particular, modified FCM algorithm is proposed to accustom the conventional FCM to the multi-class classification problem. Based on the grouping information obtained from clustering, a type of two-step supervised learning approach is proposed. Specifically, a top-classifier and three sub-classifiers integrated with windowing method and majority voting are employed to accomplish the two-step classification. The results demonstrate that the proposed method achieves 100% test accuracy and the strongest robustness compared to the conventional machine learning approaches, which shows the potential for industrial and healthcare applications, such as movement intention detection, grasp recognition and dexterous prostheses control.

Classification of electromyographic hand gesture signals using machine learning techniques

The electromyogram (EMG) signals from an individual’s muscles can reflect the biomechanics of human movement. The accurate classification of individual and combined finger movements using surface EMG signals is able to support many applications such as dexterous prosthetic hand control. The existing research of EMG-based hand gesture classification faces the challenges of inaccurate classification, insufficient generalization ability and weak robustness. To address these problems, this paper proposes a deep learning model that combines convolutional auto-encoder and convolutional neural network (CAE+CNN) to classify an EMG dataset consisting of 10 classes of hand gestures. The proposed method shrinks the inputs into a smaller latent space representation using CAE and the resultant compressed features are served as inputs of CNN, which reduces the redundancy of EMG signals and improves the classification accuracy and training efficiency. Besides, to enhance the robustness and generalization ability for classification, a data processing approach is proposed which combines the windowing method and majority voting of the obtained results from the classifier. In addition, comprehensive comparative study is carried out with 8 widely applied and state-of-the-art classifiers in terms of classification accuracy, robustness subject to noise and statistical analysis (sensitivity, specificity, precision, F1 Score and Matthews correlation coefficient). The results demonstrates that the integration of windowing method, CAE+CNN and majority voting achieves the best performance (99.38% test accuracy for the data without adding noise, which is 3.78% higher than the best classifier used for comparison), strongest robustness (achieved 98.13% test accuracy when Gaussian noise of level 1e-5 is added to the raw dataset, which is 4.07% higher than the best classifier used for comparison) and statistical properties compared to other classifiers, which shows the potential for healthcare applications such as movement intention detection and dexterous prostheses control.

A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition.

Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery.

Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

We propose a methodological study for the optimization of surface EMG (sEMG)-based hand gesture classification, effective to implement a human–computer interaction device for both healthy subjects and transradial amputees. The widely commonly used unsupervised Principal Component Analysis (PCA) approach was compared to the promising supervised common spatial pattern (CSP) methodology to identify the best classification strategy and the related tuning parameters. A low density array of sEMG sensors was built to record the muscular activity of the forearm and classify five different hand gestures. Twenty healthy subjects were recruited to compute optimized parameters for (“within” analysis) and to compare between (“between” analysis) the two strategies. The system was also tested on a transradial amputee subject, in order to assess the robustness of the optimization in recognizing disabled users’ gestures.Results show that RMS-WA/ANN is the best feature vector/classifier pair for the PCA approach (accuracy 88.81±6.58%), whereas M-RMS-WA/ANN is the best pair for the CSP methodology (accuracy of 89.35±6.16%). Statistical analysis on classification results shows no significant differences between the two strategies. Moreover we found out that the optimization computed for healthy subjects was proven to be sufficiently robust to be used on the amputee subject. This motivates further investigation of the proposed methodology on a larger sample of amputees. Our results are useful to boost EMG-based hand gesture recognition and constitute a step toward the definition of an efficient EMG-controlled system for amputees.