Research Article
FPGA-Based Realtime Detection of Freezing of Gait of Parkinson Patients
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@INPROCEEDINGS{10.1007/978-3-030-95593-9_9, author={Patrick Langer and Ali Haddadi Esfahani and Zoya Dyka and Peter Langend\o{}rfer}, title={FPGA-Based Realtime Detection of Freezing of Gait of Parkinson Patients}, proceedings={Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. 16th EAI International Conference, BODYNETS 2021, Virtual Event, October 25-26, 2021, Proceedings}, proceedings_a={BODYNETS}, year={2022}, month={2}, keywords={Freezing of Gait Temporal convolution models FPGA based implementation Tool assisted implementation Body worn sensor nodes}, doi={10.1007/978-3-030-95593-9_9} }- Patrick Langer
Ali Haddadi Esfahani
Zoya Dyka
Peter Langendörfer
Year: 2022
FPGA-Based Realtime Detection of Freezing of Gait of Parkinson Patients
BODYNETS
Springer
DOI: 10.1007/978-3-030-95593-9_9
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.
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