Parkinson's Freezing of Gait Prediction

This repository contains the code and resources for the Parkinson's Freezing of Gait Prediction project.
The project aims to develop a model that can detect and classify freeze of gait (FOG) episodes in patients with Parkinson's disease.

Competition Overview
The project is based on the TLVMC - Parkinson's Freezing Gait Prediction competition on Kaggle.

The competition focuses on two tasks:

Task 1: FOG Episode Detection and Classification

The goal is to detect FOG episodes and classify their types along the temporal axis.

The types of FOG episodes are as follows:

• StartHesitation: Uncertainty in initiating walking.
• Turn: Uncertainty during turning or moving to a destination.
• Walking: Delayed and extremely small steps.

The evaluation metric for this task is the mean Average Precision (mAP), which is the average sum of the Average Precision (AP) for each of the three event classes.

Task 2: Motor and Non-Motor Symptoms Identification in Parkinson's Patients

The objective is to explore the data and train a model to classify motor and non-motor symptoms in patients with Parkinson's disease.