A Review on the Recent use of Machine Learning for Gesture Recognition using Myoelectric Signals

• Gabriel Molina de Lima UTFPR
• Daniel Prado Campos UTFPR
• Rafael Gomes Mantovani UTFPR

DOI: https://doi.org/10.5753/eniac.2024.245071

Resumo

Gesture recognition using myoelectric signals (sEMG) is a powerful tool for Human-Machine Interfaces (HMIs). While significant progress has been made with various machine learning algorithms, more recent and robust solutions in the sEMG pipeline must be explored. This study reviews recent gesture recognition research to identify gaps and analyze standard classification and feature extraction approaches from sEMG signals. We performed a review considering studies published between 2018 and 2024. Our findings reveal a prevalence of public datasets and time-domain features. We highlight the need for further research on feature engineering, algorithm exploration beyond traditional choices, and integration of DL for feature extraction.

Palavras-chave: Electromyography (EMG), Machine Learning, Feature Extraction

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