A machine learning project to detect early signs of neurodegenerative disorders (e.g., Alzheimer’s, Parkinson’s) using voice analysis and typing pattern analysis. This repository contains code for data preprocessing, feature extraction, model training, and evaluation.
Neurodegenerative disorders often manifest subtle changes in speech and motor coordination long before clinical diagnosis. This project leverages:
- Voice Analysis: Pitch, tone, speech pauses, and vocal tremors.
- Typing Analysis: Keystroke dynamics, typing speed, and error patterns.
The goal is to build a non-invasive, low-cost screening tool for early detection.
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Voice Module:
- Preprocessing of audio recordings (noise removal, segmentation).
- Extraction of MFCCs, prosodic features, and spectral characteristics.
- CNN/LSTM models for voice-based classification.
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Typing Module:
- Capture typing patterns (key hold time, latency between keystrokes).
- Feature engineering for motor coordination metrics.
- Random Forest/GRU models for keystroke dynamics classification.
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Fusion Model:
- Late-fusion architecture combining voice and typing modalities for improved accuracy.
- Clone the repository:
git clone https://github.com/yourusername/repo-name.git cd repo-name