SEMG based classification of hand gestures using artificial neural network

Emayavaramban, G.; Divyapriya, S.; Mansoor, V.M.; Amudha, A.; Ramkumar, M. Siva; Nagaveni, P.; Sivaramkrishnan, M.

doi:10.1016/j.matpr.2020.08.504

Cited by 7 publications

(1 citation statement)

References 21 publications

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“…More specifically, to improve efficiency, intuitiveness, and the control performance of hand prosthetic systems, several Artificial Intelligence (AI) algorithms ranging from conventional Machine Learning (ML) models to highly complicated Deep Neural Network (DNN) architectures have been designed for sEMG-based hand gesture recognition in myoelectric prosthetic devices 2 – 5 . The ML-based models encompass traditional approaches such as Support Vector Machines (SVMs), Linear Discriminant Analysis (LDA), and k -Nearest Neighbors (kNNs) 6 – 9 , and DNN-based models consist of frameworks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based architectures 10 – 15 .…”

Section: Introductionmentioning

confidence: 99%

Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals

Montazerin

Rahimian

Naderkhani

et al. 2023

Sci Rep

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Designing efficient and labor-saving prosthetic hands requires powerful hand gesture recognition algorithms that can achieve high accuracy with limited complexity and latency. In this context, the paper proposes a Compact Transformer-based Hand Gesture Recognition framework referred to as $$\text {CT-HGR}$$ CT-HGR , which employs a vision transformer network to conduct hand gesture recognition using high-density surface EMG (HD-sEMG) signals. Taking advantage of the attention mechanism, which is incorporated into the transformer architectures, our proposed $$\text {CT-HGR}$$ CT-HGR framework overcomes major constraints associated with most of the existing deep learning models such as model complexity; requiring feature engineering; inability to consider both temporal and spatial information of HD-sEMG signals, and requiring a large number of training samples. The attention mechanism in the proposed model identifies similarities among different data segments with a greater capacity for parallel computations and addresses the memory limitation problems while dealing with inputs of large sequence lengths. $$\text {CT-HGR}$$ CT-HGR can be trained from scratch without any need for transfer learning and can simultaneously extract both temporal and spatial features of HD-sEMG data. Additionally, the $$\text {CT-HGR}$$ CT-HGR framework can perform instantaneous recognition using sEMG image spatially composed from HD-sEMG signals. A variant of the $$\text {CT-HGR}$$ CT-HGR is also designed to incorporate microscopic neural drive information in the form of Motor Unit Spike Trains (MUSTs) extracted from HD-sEMG signals using Blind Source Separation (BSS). This variant is combined with its baseline version via a hybrid architecture to evaluate potentials of fusing macroscopic and microscopic neural drive information. The utilized HD-sEMG dataset involves 128 electrodes that collect the signals related to 65 isometric hand gestures of 20 subjects. The proposed $$\text {CT-HGR}$$ CT-HGR framework is applied to 31.25, 62.5, 125, 250 ms window sizes of the above-mentioned dataset utilizing 32, 64, 128 electrode channels. Our results are obtained via 5-fold cross-validation by first applying the proposed framework on the dataset of each subject separately and then, averaging the accuracies among all the subjects. The average accuracy over all the participants using 32 electrodes and a window size of 31.25 ms is 86.23%, which gradually increases till reaching 91.98% for 128 electrodes and a window size of 250 ms. The $$\text {CT-HGR}$$ CT-HGR achieves accuracy of 89.13% for instantaneous recognition based on a single frame of HD-sEMG image. The proposed model is statistically compared with a 3D Convolutional Neural Network (CNN) and two different variants of Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) models. The accuracy results for each of the above-mentioned models are paired with their precision, recall, F1 score, required memory, and train/test times. The results corroborate effectiveness of the proposed $$\text {CT-HGR}$$ CT-HGR framework compared to its counterparts.

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Section: Introductionmentioning

confidence: 99%

Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals

Montazerin

Rahimian

Naderkhani

et al. 2023

Sci Rep

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A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Yaghoubi,

Khamees

et al. 2024

Neural Comput & Applic

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Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble learning (EL) are four outstanding approaches that enable algorithms to extract information from data and make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, and EL models have found extensive application in predicting geotechnical and geoenvironmental parameters. This research aims to provide a comprehensive assessment of the applications of ANN, ML, DL, and EL in addressing forecasting within the field related to geotechnical engineering, including soil mechanics, foundation engineering, rock mechanics, environmental geotechnics, and transportation geotechnics. Previous studies have not collectively examined all four algorithms—ANN, ML, DL, and EL—and have not explored their advantages and disadvantages in the field of geotechnical engineering. This research aims to categorize and address this gap in the existing literature systematically. An extensive dataset of relevant research studies was gathered from the Web of Science and subjected to an analysis based on their approach, primary focus and objectives, year of publication, geographical distribution, and results. Additionally, this study included a co-occurrence keyword analysis that covered ANN, ML, DL, and EL techniques, systematic reviews, geotechnical engineering, and review articles that the data, sourced from the Scopus database through the Elsevier Journal, were then visualized using VOS Viewer for further examination. The results demonstrated that ANN is widely utilized despite the proven potential of ML, DL, and EL methods in geotechnical engineering due to the need for real-world laboratory data that civil and geotechnical engineers often encounter. However, when it comes to predicting behavior in geotechnical scenarios, EL techniques outperform all three other methods. Additionally, the techniques discussed here assist geotechnical engineering in understanding the benefits and disadvantages of ANN, ML, DL, and EL within the geo techniques area. This understanding enables geotechnical practitioners to select the most suitable techniques for creating a certainty and resilient ecosystem.

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Energy management system for charging stations for electric and hydrogen vehicles using solar, hydrogen and fuel cell technology

Raj¹,

Saravanan

Durairaj

et al. 2022

Industrial, Mechanical and Electrical Engineering

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SEMG based classification of hand gestures using artificial neural network

Cited by 7 publications

References 21 publications

Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals

Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals

A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Energy management system for charging stations for electric and hydrogen vehicles using solar, hydrogen and fuel cell technology

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