FPGA-Based Realtime Detection of Freezing of Gait in Parkinson Patients Using Neural Networks

Abstract

In this paper we report on our implementation of a temporal convolutional network trained to detect Freezing of Gait on an FPGA. In order to be able to compare our results with state of the art solutions we used the well-known open dataset Daphnet. Our most important findings are even though we used a tool to map the trained model to the FPGA we can detect FoG in less than a millisecond which will give us sufficient time to trigger cueing and by that prevent the patient from falling. In addition, the average sensitivity achieved by our implementation is comparable to solutions running on high end devices.

Patrick Langer

Co-Founder, Scientific Director & AI Researcher

I develop time-series foundation models and the systems required to train, evaluate, and deploy them across healthcare, industry, and other evolving domains.