ArticleSensors (Basel, Switzerland)2022
Real-Time Analysis of Hand Gesture Recognition with Temporal Convolutional Networks.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- EMG-based hand gesture recognition using multi-scale deep residual network with SE-module.Scientific reports · 2026Article
- Sagittal-Plane Knee Flexion Moment Estimation Using a Lightweight Deep Learning Framework Based on Sequential Surface EMG Feature Frames.Sensors (Basel, Switzerland) · 2026Article
- Multijoint Continuous Motion Estimation for Human Lower Limb Based on Surface Electromyography.Sensors (Basel, Switzerland) · 2025Article
- On the Distribution of Muscle Signals: A Method for Distance-Based Classification of Human Gestures.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. (1) Background: In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. Recently, a specific class of these models called Temporal Convolutional Networks (TCNs) has been successfully applied to this task. (2) Methods: In this paper, we approach electromyography-based hand gesture recognition as a sequence classification problem using TCNs. Specifically, we investigate the real-time behavior of our previous TCN model by performing a simulation experiment on a recorded sEMG dataset. (3) Results: The proposed network trained with data augmentation yields a small improvement in accuracy compared to our existing model. However, the classification accuracy is decreased in the real-time evaluation, showing that the proposed TCN architecture is not suitable for such applications. (4) Conclusions: The real-time analysis helps in understanding the limitations of the model and exploring new ways to improve its performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.