Deep Learning Models for the Early Detection of Parkinson’s Disease using the motor-based symptoms.

Detection of Degree of Parkinsonism via the Spiral Test

Parkinson's disease clinical study app

Analytics done on clinical healthcare parkinsons analytics

Python library for real-time gait modulation prediction using multimodal neural and movement data (LFP, EEG, IMU, EMG) — designed for closed-loop DBS systems in Parkinson’s disease.

Parkinson's Progression Marker Initiative data science challenge, 2016

The project strives to predict the risk of Parkinson's Disease progression in the patient based on the evaluation of baseline motor and non-motor symptoms of the patients via machine learning approach.

An iOS App that acts as a customizable metronome. Helpful for establishing a walking rhythm for people with Parkinson's Disease.

Built a Parkinson's Disease Detection tool using SVM

Adaptive deep learning models for Parkinson’s diagnosis using smartwatch sensor data (PADS Dataset)

Simple self measurement of parkinsons symptoms

MultiAgent-Based Model (MABM) designed to simulate Parkinson's disease using Repast Simphony.

A simple Flask API for predicting whether a person could be a Parkinson's patient or not based on some basic drawings of random shapes drawn by them.

CMPT 275 Project: Health bars

Research code for analysing pain, opioid prescriptions, and Parkinson's disease using UK Biobank data

This is a classification project to compare some algorithms of the sklearn API in the parkinsons dataset.

🏆 HackWestern 1st Place (Data Visualization) - 🏥 Developed a Human Machine Interface though position tracking to accurately diagnose degenerative disorders such as Parkinson's. Built using Leap Motion hardware integrated with FFT ML algorithm with Python and displayed with Javascript and Angular 7.

You, me, and Parkinson's Disease