Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

Dorgham, Osama; Al-Mherat, Ibrahim; Alshaer, Jawdat; Bani‐Ahmad, Sulieman; Laycock, Stephen D.

doi:10.3390/fi11010025

Cited by 12 publications

(4 citation statements)

References 16 publications

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“…It's not just handy that the good effects happen; they often have big effects [8], [9], [10]. However, the complexity of human language, which includes subtleties in syntax, meaning, and pragmatics, makes it very hard to get very accurate question classification [11], [12]. Support Vector Machines, Random Forests, and other machine learning models have been used for this, but new developments in deep learning and transformer models like BERT, RoBERTa, and ELECTRA have shown that they work even better than expected [13].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Approach to Question Classification: Integrating Electra Transformer, GloVe, and LSTM

Aburass,

Dorgham,

Rumman

2024

IJACSA

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Natural Language Processing (NLP) has emerged as a critical technology for understanding and generating human language, with applications including machine translation, sentiment analysis, and, most importantly, question classification. As a subfield of NLP, question classification focuses on determining the type of information being sought, which is an important step for downstream applications such as question answering systems. This study introduces an innovative ensemble approach to question classification that combines the strengths of the Electra, GloVe, and LSTM models. After being tried thoroughly on the well-known TREC dataset, the model shows that combining these different technologies can produce better outcomes. For understanding complex language, Electra uses transformers; GloVe uses global vector representations for wordlevel meaning; and LSTM models long-term relationships through sequence learning. Our ensemble model is a strong and effective way to solve the hard problem of question classification by mixing these parts in a smart way. The ensemble method works because it got an 80% accuracy score on the test dataset when it was compared to well-known models like BERT, RoBERTa, and DistilBERT.

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Section: Introductionmentioning

confidence: 99%

An Ensemble Approach to Question Classification: Integrating Electra Transformer, GloVe, and LSTM

Aburass,

Dorgham,

Rumman

2024

IJACSA

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“…[ 24 ] Various methods have been proposed to predict EMG signals, and some of the related parameters, namely signal parameters such as biceps force, have been estimated for different loads to predict the angle of the joints. [ 25 26 27 28 ] In some other methods, different parameters belonging to the signal, such as waveform length, root minimum square, slope sign change, zero crossing, and simple square integral, have been applied. [ 29 30 31 32 33 34 35 ] Machine learning algorithm methods, including the support vector machine and random forest,[ 36 ] has been applied to predict muscle activity, none of which has predicted the EMG signal.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Biceps Muscle Electromyogram Signal Using a NARX Neural Network

Rahatabad¹,

Khodadadi²,

Sheikhani³

et al. 2023

Journal of Medical Signals &Amp; Sensors

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Background: This study was conducted to compare the response between the results of experimental data and the results achieved by the NARX neural network model to predict the electromyogram (EMG) signal on the biceps muscle in nonlinear stimulation conditions as a new stimulation model. Methods: This model is applied to design the controllers based on functional electrical stimulation (FES). To this end, the study was conducted in five stages, including skin preparation, placement of recording and stimulation electrodes, along with the position of the person to apply the stimulation signal and recording EMG, stimulation and recording of single-channel EMG signal, signal preprocessing, and training and validation of the NARX neural network. The electrical stimulation applied in this study is based on a chaotic equation derived from the Rossler equation and on the musculocutaneous nerve, and the response to this stimulation, i.e., the EMG signal, is from the biceps muscle as a single channel. The NARX neural network was trained, along with the stimulation signal and the response of each stimulation for 100 recorded signals from 10 individuals, and then validated and retested for trained data and new data after processing and synchronizing both signals. Results: The results indicate that the Rossler equation can create nonlinear and unpredictable conditions for the muscle, and we also can predict the EMG signal with the NARX neural network as a predictive model. Conclusion: The proposed model appears to be a good method to predict control models based on FES and to diagnose some diseases.

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“…For example, some studies tested both TD and FD features as input data [ 19 , 20 ], and others applied raw EMG signals directly to the ANN without a feature extraction process [ 21 , 22 ]. Additionally, the application of research was not limited to hand/finger gesture recognition but included the prediction of force load [ 23 , 24 ] and the detection of neuromuscular disorders [ 25 ]. However, studies that applied ANN algorithms to TD features only, excluding other features or data types, for hand/finger gesture recognition, have not been conducted as extensively as other EMG studies, which used both TD and FD features or raw EMG signals for various purposes [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks

Lee

Min

Byun

2021

Sensors

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Electromyogram (EMG) signals have been increasingly used for hand and finger gesture recognition. However, most studies have focused on the wrist and whole-hand gestures and not on individual finger (IF) gestures, which are considered more challenging. In this study, we develop EMG-based hand/finger gesture classifiers based on fixed electrode placement using machine learning methods. Ten healthy subjects performed ten hand/finger gestures, including seven IF gestures. EMG signals were measured from three channels, and six time-domain (TD) features were extracted from each channel. A total of 18 features was used to build personalized classifiers for ten gestures with an artificial neural network (ANN), a support vector machine (SVM), a random forest (RF), and a logistic regression (LR). The ANN, SVM, RF, and LR achieved mean accuracies of 0.940, 0.876, 0.831, and 0.539, respectively. One-way analyses of variance and F-tests showed that the ANN achieved the highest mean accuracy and the lowest inter-subject variance in the accuracy, respectively, suggesting that it was the least affected by individual variability in EMG signals. Using only TD features, we achieved a higher ratio of gestures to channels than other similar studies, suggesting that the proposed method can improve the system usability and reduce the computational burden.

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Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

Cited by 12 publications

References 16 publications

An Ensemble Approach to Question Classification: Integrating Electra Transformer, GloVe, and LSTM

An Ensemble Approach to Question Classification: Integrating Electra Transformer, GloVe, and LSTM

Prediction of Biceps Muscle Electromyogram Signal Using a NARX Neural Network

Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks

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