FPGA-Based Realtime Detection of Freezing of Gait in Parkinson Patients Using Neural Networks
Publications
- Patrick Langer, Ali Haddadi Esfahani, Zoya Dyka, Peter Langendorfer (2022). FPGA-Based Realtime Detection of Freezing of Gait of Parkinson Patients. EAI Bodynets
Summary
This work was done as part of my master's thesis, and evantually published in EAI bodynets. Freezing of Gait is a condition in which patients freeze during their movement, potentially leading to falls. During my thesis, I investigated and trained various neural networks for time-series classification tasks, such as Temporal Convulational Networks (TCNs), Time-Delay Neural Networks (TDNNs) and Long-Short Term Memory Networks (LSTMs). The goal is to release a cueing signal based on audio or electrical feedback to release the freezing upon it's detection. Due to realtime requirements (cueing needs to be issued within 20ms), the neural network was implemented on an FPGA using VitisAI.
Abstract
In our 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
Doctoral Researcher
My research interests include machine learning systems, mobile- and wearableAI, realtime AI and digital health.
