AUTHOR=Schulte Robert V. , Prinsen Erik C. , Hermens Hermie J. , Buurke Jaap H. TITLE=Genetic Algorithm for Feature Selection in Lower Limb Pattern Recognition JOURNAL=Frontiers in Robotics and AI VOLUME=Volume 8 - 2021 YEAR=2021 URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2021.710806 DOI=10.3389/frobt.2021.710806 ISSN=2296-9144 ABSTRACT=Choosing the right features is important to optimize lower limb pattern recognition, such as in prosthetic control. EMG signals are noisy in nature, which makes it more challenging to extract useful information. Many features are used in literature, which raises the question which features are most suited for use in lower limb myoelectric control. Therefore, it is important to find combinations of best performing features. One way to achieve this is by using a genetic algorithm, a meta-heuristic capable of searching vast feature spaces. The goal of this research is to demonstrate the capabilities of a genetic algorithm and come up with a feature set that has a better performance compared with state-of-the-art. In this study we collected a data set containing ten able-bodied subjects who performed various gait related activities, while measuring EMG and kinematics. The genetic algorithm selected features based on the performance on the training partition of this data set. The selected feature sets were evaluated on the remaining test set and on the online benchmark data set ENABL3S, against a state-of-the-art feature set. Results show that a feature set based on the selected features of a genetic algorithm outperforms state of the art. The overall error decreased up to 0.54% and the transitional error by 2.44%, which represents a relative decrease in overall errors up to 11.6% and transitional errors up to 14.1%, although these results were not significant. This study showed that a genetic algorithm is capable of searching a large feature space and that systematic feature selection shows promising results for lower limb myoelectric control.