Comparing subject-to-subject transfer learning methods in surface electromyogram-based motion recognition with shallow and deep classifiers

• Published in: Neurocomputing (Amsterdam) Vol. 489; pp. 599 - 612
• Main Authors: Hoshino, Takayuki, Kanoga, Suguru, Tsubaki, Masashi, Aoyama, Atsushi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 07.06.2022
• Subjects: Fine tuning; Motion recognition; Style transfer mapping; Surface electromyogram; Transfer learning; Surface electromyogram; Style transfer mapping; Transfer learning; Motion recognition; Fine tuning
• DOI: 10.1016/j.neucom.2021.12.081
• ISSN: 0925-2312

Surface electromyogram (sEMG)-based human-computer interface (HCI) is an effective tool for detecting human movements. Because sEMG-based motion recognition usually requires prolonged data measurements from the user (target), transfer learning reusing pre-measured (source) data from other users and pre-trained classifiers can be applied to sEMG data to reduce the measurement time. However, little knowledge is available regarding the combination of transfer learning methods and classifiers in sEMG data applications. Thus, we investigated the classification accuracy of data- and parameter-space-based transfer learning with shallow or deep classifiers in cross-subject sEMG classification. The dataset contains eight classes of forearm motions recorded from 25 volunteer participants. We used a support vector machine (SVM) as a shallow classifier as well as a deep neural network architecture, referred to as an artificial neural network (ANN), as a deep classifier. In addition, we used style transfer mapping (STM) as a data-space-based transfer learning method and fine-tuning (FT) as a parameter-space-based transfer learning method. Consequently, the classification accuracy of the ANN was higher than that of the SVM, regardless of the combinational use of transfer learning. STM and FT significantly improved the classification accuracy compared with non-transfer cases regardless of the classifier (note that FT can only be used with the ANN). In particular, the combined use of FT and the ANN yielded the best accuracy. These findings suggest that parameter-space-based transfer learning and deep classifiers are suitable for cross-subject sEMG classification. The combined use of parameter-space-based transfer learning and deep classifiers can effectively reduce the data measurement time of sEMG-based HCI applications.