Détail du document
Identifiant
oai:arXiv.org:2211.06951
Sujet
Electrical Engineering and Systems...Auteur
Ghuman, Purnoor Lyall, Tyama Mahboob, Usama Aamir, Alia Liu, XilinCatégorie
sciences : génie électrique et science des systèmes
Année
2022
Date de référencement
25/09/2023
Mots clés
parkinsonRésumé
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.
Ghuman, Purnoor,Lyall, Tyama,Mahboob, Usama,Aamir, Alia,Liu, Xilin, 2022, ECE496Y Final Report: Edge Machine Learning for Detecting Freezing of Gait in Parkinson's Patients
MELAS: Phenotype Classification into Classic-versus-Atypical Presentations
presentations mitochondrial strokelike patients variability phenotype clinical melas
