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
Configurable Safety Tuning of Language Models with Synthetic Preference Data
data preference cst dpo configurable language safety
Affective flexibility as a developmental building block of cognitive reappraisal: An fMRI study
cognitive success affective flexibility
