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 the 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 than the state-of-the-art feature set. In this study, we collected a dataset 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 dataset. The selected feature sets were evaluated on the remaining test set and on the online benchmark dataset ENABL3S, against a state-of-the-art feature set. The results show that a feature set based on the selected features of a genetic algorithm outperforms the state-of-the-art set. The overall error decreased up to 0.54% and the transitional error by 2.44%, which represent 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.
Highlights
Motor intent recognition using electromyography (EMG) has the potential to create intuitive control of prosthetic devices
This study shows that feature selection using a genetic algorithm reduces overall and transitional errors, and it is worthwhile to use a genetic algorithm for feature selection in lower limb myoelectric control
The goal of this study was to investigate the use of a genetic algorithm for feature selection to enhance myoelectric control of the lower limb
Summary
Motor intent recognition using electromyography (EMG) has the potential to create intuitive control of prosthetic devices. EMG signals are noisy in nature, which makes it challenging to extract user intent (Phinyomark and Scheme, 2018). To solve this challenge, feature extraction can be used to improve information density and reduce noise, which leads to better intent recognition. Numerous feature extraction methods and feature combinations have been proposed (Phinyomark and Scheme, 2018; Hudgins et al, 1993; Phinyomark et al, 2017; Phinyomark et al, 2018). The best combination of features can be found by trying out every combination; but with the increase in possible features over the years, this becomes unfeasible. Feature selection and dimension reduction techniques have been used to remove redundant and irrelevant features
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