This project focuses on detecting Parkinson's disease using voice and spiral handwriting data. It combines a Support Vector Machine (SVM) model for voice-based detection and a Convolutional Neural Network (CNN) model for spiral handwritten image-based detection. The models are then fused using the late fusion technique, with a Logistic Regression model to provide the final output.
Install the required dependencies:
pip install -r requirements.txt- Prepare the input data:
- Record the audio of the patient using the provided voice recording functionality.
- Capture the image of the spiral handwriting using the integrated image capture feature.
- Run the inference:
- Provide the recorded audio and captured image as input to the pre-trained models.
- The SVM model analyzes the voice data to detect Parkinson's disease.
- The CNN model processes the spiral handwriting image for Parkinson's detection.
- Apply the late fusion technique to combine the outputs of the SVM and CNN models.
- Use the Logistic Regression model to generate the final output.
- No additional configuration is required for the pre-trained models.
- Adjust the threshold or decision criteria in the code based on the project's specific requirements.
We welcome contributions from the community. If you'd like to contribute to the project, please follow these guidelines:
- Fork the repository and create a new branch for your feature or bug fix.
- Ensure your code follows the project's coding standards.
- Submit a pull request, describing the changes you've made and their purpose.
To run the tests, use the following command:
python tests.py
Make sure the dependencies are installed before running the tests.
If you encounter any issues or errors, please refer to the troubleshooting guide for possible solutions.
This project is licensed under the MIT License.
We would like to acknowledge the following individuals and resources for their contributions to this project:
For any questions or inquiries, please contact us at [email protected].