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


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.

In EAI Bodynets
Patrick Langer
Patrick Langer
Doctoral Researcher

My research interests include machine learning systems, mobile- and wearableAI, realtime AI and digital health.