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.

Built a Parkinson's Disease Detection tool using SVM

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

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.

CMPT 275 Project: Health bars

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.

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

You, me, and Parkinson's Disease

🏆 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.

An LRA-based DIY vibrotactile parkinsons glove design that features a new spring-based LRA tactor and a Bluetooth speakerphone.