A Neural Network Based on the Johnson $S_\mathrm{U}$ Translation System and Related Application to Electromyogram Classification

TL;DR

This paper introduces a neural network leveraging the Johnson $S_U$ translation system to effectively model skewed and kurtotic data distributions, improving EMG classification without hyperparameter tuning.

Contribution

It proposes a novel neural network architecture based on the Johnson $S_U$ system that captures complex data distributions, with theoretical convergence guarantees.

Findings

High classification accuracy on artificial data.

Effective EMG classification on real biological data.

No hyperparameter tuning required.

Abstract

Electromyogram (EMG) classification is a key technique in EMG-based control systems. The existing EMG classification methods do not consider the characteristics of EMG features that the distribution has skewness and kurtosis, causing drawbacks such as the requirement of hyperparameter tuning. In this paper, we propose a neural network based on the Johnson $S_\mathrm{U}$ translation system that is capable of representing distributions with skewness and kurtosis. The Johnson system is a normalizing translation that transforms non-normal data to a normal distribution, thereby enabling the representation of a wide range of distributions. In this study, a discriminative model based on the multivariate Johnson $S_\mathrm{U}$ translation system is transformed into a linear combination of coefficients and input vectors using log-linearization. This is then incorporated into a neural network structure, thereby allowing the calculation of the posterior probability of the input vectors for each class and the determination of model parameters as weight coefficients of the network. The uniqueness of convergence of the network learning is theoretically guaranteed. In the experiments, the suitability of the proposed network for distributions including skewness and kurtosis is evaluated using artificially generated data. Its applicability for real biological data is also evaluated via an EMG classification experiment. The results show that the proposed network achieves high classification performance without the need for hyperparameter optimization.