A Style Transfer Mapping and Fine-Tuning Subject Transfer Framework Using Convolutional Neural Networks for Surface Electromyogram Pattern Recognition

Suguru Kanoga; Takayuki Hoshino; Mitsunori Tada

ICASSP 2022accepted0 citations

A Style Transfer Mapping and Fine-Tuning Subject Transfer Framework Using Convolutional Neural Networks for Surface Electromyogram Pattern Recognition

Suguru Kanoga, Takayuki Hoshino, Mitsunori Tada

Abstract

Reducing inter-subject variability between new users and the measured source subjects, and effectively using the information of classification models trained by source subject data, is very important for human–machine interfaces. In this study, we propose a style transfer mapping (STM) and fine-tuning (FT) subject transfer framework using convolutional neural networks (CNNs). To evaluate the performance, we used two types of public surface electromyogram datasets named MyoDatasets and NinaPro database 5. Our proposed framework, STM-FT-CNN, showed the best performances in all cases compared with conventional subject transfer frameworks. In the future, we will build an online processing system that includes this subject transfer framework and verify its performance in online experiments.

BibTeX

@inproceedings{icassp2022_astyletransferma,
  title = {A Style Transfer Mapping and Fine-Tuning Subject Transfer Framework Using Convolutional Neural Networks for Surface Electromyogram Pattern Recognition},
  author = {Suguru Kanoga and Takayuki Hoshino and Mitsunori Tada},
  booktitle = {ICASSP 2022},
  year = {2022}
}