Application of Min-Max Normalization on Subject-Invariant EMG Pattern Recognition

Surface electromyography (EMG) is one of the promising signals for the recognition of the intended hand movement of an amputee. Nevertheless, there are several barriers to its successful implementation in the advanced prosthetic hand. Subject-dependent EMG pattern recognition is one of them, which limits the use of a training model for a specific subject to others. So, this study aims to explore a subject invariant EMG pattern recognition method that is performed by extracting subject invariant features. To extract subject invariant features, we have created a feature space using a feature extraction method, and the dimensionality of the feature space is reduced by employing spectral regression discriminant analysis (SRDA). Finally, each SRDA feature is normalized using min-max normalization, which confines the scale of each SRDA feature from 0 to 1. The proposed subject invariant EMG pattern recognition method achieves the F1 score of 97.26%, 96.47%, 95.42%, and 93.71% with a linear discriminant analysis classifier (LDA) for an electrode array of 8 x 16, 8 x 8, 8 x 4, and 8 x 2, respectively. The achieved performances are almost equal to or sometimes better than those achieved in subject independent and subject-dependent EMG pattern recognition. Also, the proposed method is simple, classifier independent, time complexity free, and does not require any customization or fine-tuning of classifiers. So, the proposed subject invariant EMG pattern recognition method would be an option to overcome the training barrier for each subject without compromising the EMG pattern recognition performance.

Automated summary

Surface electromyography (EMG) is a promising signal for hand movement recognition. However, subject-dependent EMG pattern recognition limits its use for different subjects. This study proposes a subject invariant EMG pattern recognition method by extracting subject invariant features and using spectral regression discriminant analysis (SRDA) for dimensionality reduction. The proposed method achieves high F1 scores and outperforms subject independent and subject-dependent methods, while being simple, classifier independent, and time complexity free.