Open AccessA Neural Network Based on the Johnson S U Translation System and Related Application to Electromyogram Classification
Author(s) -
Hideaki Hayashi,
Taro Shibanoki,
Toshio Tsuji
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3126348
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Electromyogram (EMG) classification is a key technique in EMG-based control systems. Existing EMG classification methods, which do not consider EMG features that have distribution with skewness and kurtosis, have limitations such as the requirement to tune hyperparameters. 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 distribution data into normal distribution data, 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; then, it is incorporated into a neural network structure. This allows the calculation of the posterior probability of each class given the input vectors 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 was evaluated using artificially generated data. Its applicability to real biological data was also evaluated via EMG classification experiments. The results showed that the proposed network achieved high classification performance (e.g., 99.973% accuracy using Khushaba’s dataset) without the need for hyperparameter optimization.