A Comparative Study of sEMG Gesture Recognition Algorithms Based on Ninapro DB1
TL;DR
This study utilized the publicly available Ninapro DB1 dataset and employed a standardized preprocessing and feature extraction workflow to systematically compare the performance of four machine learning algorithms — Random Forest, Multi-Layer Perceptron, LightGBM, and K-Nearest Neighbors — in sEMG gesture recognition tasks.
Abstract
: Due to the accelerating aging of the population and the continuous increase in the number of stroke patients, there is an urgent need for rehabilitation robotics technology with high-precision gesture recognition capabilities. Surface electromyography (sEMG) signals, as an important type of bioelectric signal, can reflect human movement intentions. However, their non-stationary nature and low signal-to-noise ratio pose challenges for recognition algorithms. This study utilized the publicly available Ninapro DB1 dataset and employed a standardized preprocessing and feature extraction workflow to systematically compare the performance of four machine learning algorithms — Random Forest (RF), Multi-Layer Perceptron (MLP), LightGBM, and K-Nearest Neighbors (KNN) — in sEMG gesture recognition tasks. Experiments were conducted using 10-fold cross-validation, with performance evaluated across multiple metrics including accuracy, precision, recall, and F1 score. The results show that LightGBM performs best across all metrics (all exceeding 88.5%), demonstrating strong feature learning and generalization capabilities. This study provides empirical evidence for the selection of sEMG gesture recognition algorithms and offers guidance for the practical application of rehabilitation robot systems.