%0 Conference Paper
%A Neftci, Emre
%A Finkbeiner, Jan Robert
%A Leroux, Nathan
%T Online Transformers with Spiking Neurons for Fast Prosthetic Hand Control
%M FZJ-2024-06528
%D 2023
%Z Also published in 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS), Electronic ISBN:979-8-3503-0026-0Print on Demand(PoD) ISBN:979-8-3503-0027-7
%X Fast and accurate online processing is essential for smooth prosthetic hand control with Surface Electromyography signals (sEMG). Although transformers are state-of-the-art deep learning models in signal processing, the self-attention mechanism at the core of their operations requires accumulating data for large time-windows. They are therefore not suited for online signal processing. In this paper, we use an attention mechanism with sliding windows that allows a transformer to process sequences element-by-element. Moreover, we increase the sparsity of the network using spiking neurons. We test the model on the NinaproDB8 finger position regression dataset. Our model sets its new state-of-the-art in terms of accuracy on NinaproDB8, while requiring only very short time windows of 3.5 ms at each inference step, and reducing the number of synaptic operations up to a factor of ×5.3 thanks to the spiking neurons. Our results hold great promises for wearable online sEMG processing systems for prosthetic hand control.
%B Artificial Intelligence BioMedical Circuits And Systems For Health
%C 19 Oct 2023 - 21 Oct 2023, Toronto (Canada)
Y2 19 Oct 2023 - 21 Oct 2023
M2 Toronto, Canada
%F PUB:(DE-HGF)24
%9 Poster
%U https://juser.fz-juelich.de/record/1033656