TY  - CONF
AU  - Neftci, Emre
AU  - Finkbeiner, Jan Robert
AU  - Leroux, Nathan
TI  - Online Transformers with Spiking Neurons for Fast Prosthetic Hand Control
M1  - FZJ-2024-06528
PY  - 2023
N1  - 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
AB  - 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.
T2  - Artificial Intelligence BioMedical Circuits And Systems For Health
CY  - 19 Oct 2023 - 21 Oct 2023, Toronto (Canada)
Y2  - 19 Oct 2023 - 21 Oct 2023
M2  - Toronto, Canada
LB  - PUB:(DE-HGF)24
UR  - https://juser.fz-juelich.de/record/1033656
ER  -