Hand Gesture Recognition via Transient sEMG Using Transfer Learning of Dilated Efficient CapsNet: Towards Generalization for Neurorobotics

There has been an accelerated surge in utilizing the deep neural network to decode central and peripheral activations of the human nervous system to boost the spatiotemporal resolution of neural interfaces used in human-centered robotic systems, such as prosthetics, and exoskeletons. Deep learning methods are proven to achieve high accuracy but are also challenged by their assumption of having access to massive training samples. Objective: In this letter, we propose Dilated Efficient CapsNet to improve the predictive performance when the available individual data is minimal and not enough to train an individualized network for controlling a personalized robotic system. Method: We proposed the concept of transfer learning for a new design of the dilated efficient capsular neural network to relax the need of having access to massive individual data and utilize the field knowledge which can be learned from a group of participants. In addition, instead of using complete sEMG signals, we only use the transient phase, reducing the volume of training samples to 20% of the original and maximizing the agility. Results: In experiments, we validate the performance with various amounts of injected personalized training data (from 25% to 100% of transient phase). The results support the use of the proposed transfer learning approach based on the dilated capsular neural network when the knowledge domain learned on a small number of subjects can be utilized to minimize the need for new data from new subjects. The model focuses only on the transient phase which is a challenging neural interfacing problem.