Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyography-based gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples. This paper's hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition. Consequently, this paper proposes applying transfer learning on…

• ID
  Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail
• NF
  Norges Forskningsråd

  Award: 262762
• FC
  Fondation Choquette-Legault
• NS
  Natural Sciences and Engineering Research Council of Canada

  Award: 401220434