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arXiv · 2211.06951

ECE496Y Final Report: Edge Machine Learning for Detecting Freezing of Gait in Parkinson's Patients

Purnoor Ghuman·Tyama Lyall·Usama Mahboob·Alia Aamir·Xilin Liu
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Abstract

Parkinson's disease is a common neurological disease, entailing a multitude of motor deficiency symptoms. In this project, we developed a device with an uploaded edge machine learning algorithm that can detect the onset of freezing of gait symptoms in a Parkinson's patient. The algorithm achieved an accuracy of 83.7% in a validation using data from ten patients. The model was deployed in a microcontroller Arduino Nano 33 BLE Sense Board model and validated in real-time operation with data streamed to the microcontroller from a computer.

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Purnoor Ghuman, Tyama Lyall, Usama Mahboob, Alia Aamir, Xilin Liu. 2022-11-13. ECE496Y Final Report: Edge Machine Learning for Detecting Freezing of Gait in Parkinson's Patients. https://arxiv.org/abs/2211.06951

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