Time-domain Mixup Source Data Augmentation of sEMGs for Motion Recognition towards Efficient Style Transfer Mapping

Motion recognition based on surface electromyogram (sEMG) recorded from the forearm is attracting attention for its applicability because it easily integrates with wearable devices and has a high signal-to-noise ratio. Inter-subject variability and inadequate data availability are common problems encountered in classifiers. Transfer learning (TL) techniques can reduce the inter-subject variability; however, when the amount of data recorded from each source subject is small, the TL-combined classifier is prone to overfitting problems. In this study, we tested the accuracy of motion recognition with and without TL when the source dataset was increased up to 10 times with a time-domain data augmentation method called mixup. The performance was evaluated using an 8-class sEMG dataset containing wearable sensing data from 25 subjects. We found that mixup improved the performance of TL-combined classifiers (support vector machine and 4-layered fully connected feedforward neural network). In future work, we plan to investigate the relationship between the amount of data and sEMG-based motion recognition by comparing multiple sEMG datasets and multiple data augmentation methods.