Journal Article

Enhanced Hand Gesture Recognition with Surface Electromyogram and Machine Learning

Imran, Ahmed, Kadavath, Mujeeb Rahman Kanhira, Nasor, Mohamed

2024

Overview

This study delves into decoding hand gestures using surface electromyography (EMG) signals collected via a precision Myo-armband sensor, leveraging machine learning algorithms. The research entails rigorous data preprocessing to extract features and labels from raw EMG data. Following partitioning into training and testing sets, four traditional machine learning models are scrutinized for their efficacy in classifying finger movements across seven distinct gestures. The analysis includes meticulous parameter optimization and five-fold cross-validation to evaluate model performance. Among the models assessed, the Random Forest emerges as the top performer, consistently delivering superior precision, recall, and F1-score values across gesture classes, with ROC-AUC scores surpassing 99%. These findings underscore the Random Forest model as the optimal classifier for our EMG dataset, promising significant advancements in healthcare rehabilitation engineering and enhancing human–computer interaction technologies.

• Publisher: MDPI AG,MDPI
• Subject: Accuracy / Adult / Algorithms / cross-validation / Datasets / Deep learning / Electrodes / electromyogram / Electromyography / Electromyography - methods / EMG / EMG sensor / Female / Gestures / Hand - physiology / hand gestures / Humans / Keyboards / Machine Learning / Male / Movement - physiology / Muscle contraction / Muscle function / Myo armband / Pattern Recognition, Automated - methods / Prostheses / Sensors / Sign language / Signal processing / Signal Processing, Computer-Assisted / Virtual reality / Wavelet transforms / Young Adult