parkinsons-disease

Novoic's audio feature extraction library

A new accessible interface for your smartphone, suitable for seniors

A research project that aims to detect Parkinson's disease in patients using Gait Analysis data. Subsequently, the project may make use of Gait Data Analysis to make powerful inferences which would help in genralizing the most common groups affected by this disease.

Multimodal Dataset of Freezing of Gait in Parkinson's Disease

Code for the paper "Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson’s Disease Motor Severity"

A Folding@Home Docker container with GPU support

A Python-based machine learning project for classifying Parkinson's disease using patient data and algorithms like XGBoost and Random Forest. Includes data preprocessing, feature analysis, and model evaluation with Scikit-learn and Pandas for accurate predictions.

Parkinson Disease Detection using Machine Learning

Sorce code of Apkinson: android app to monitor the motor symptoms of Parkinson's patients

Parkinson's disease data analysis from uci machine learning repository dataset.

Analysis of Parkinson's Progressive Markers Initiative Data

An Explainable Geometric-Weighted Graph Attention Network (xGW-GAT) for Identifying Functional Networks Associated with Gait Impairment

Parkinson disease is associated with movement disorder symptoms, such as tremor, rigidity, bradykinesia, and postural instability. The manifestation of bradykinesia and rigidity is often in the early stages of the disease. These have a noticeable effect on the handwriting and sketching abilities of patients, and micrographia has been used for early-stage diagnosis of Parkinson’s disease. While handwriting of a person is influenced by a number of factors such as language proficiency and education, sketching of a shape such as the spiral has been found to be non-invasive and independent measure.

Unveiling the Tremors, A Reliable Algorithm with 83% Accuracy for Detecting Parkinson's Disease through Spiral/Wave Sketch Images.

Detection of Degree of Parkinsonism via the Spiral Test

We propose LightCNN, a lightweight CNN architecture designed for efficient and effective Parkinson's disease classification using EEG data. Article: https://doi.org/10.48550/arXiv.2408.10457

Android app that tracks tremors and recommends follow up action

An AI-based mobile application that is able to diagnose Parkinson's Disease using two independent tests that require only a pencil and a paper. Based on the 2017 research paper Distinguishing Different Stages of Parkinson's Disease Using Composite Index of Speed and Pen-Pressure of Sketching a Spiral by Zham et. al. The trained models were deployed using a Flask backend server, along with a Flutter based frontend mobile application frontend to interact with the REST API.

Computer Intelligence subject final project at UPC.

Detection of Parkinson’s Disease Using Vocal Features: An Eigen Approach 🤖🧠