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https://github.com/mshuqair/multi-shared-task-self-supervised-cnn-lstm
Multi-shared-task Self-supervised Learning utilizing CNN-LSTM network
https://github.com/mshuqair/multi-shared-task-self-supervised-cnn-lstm
cnn-lstm deep-learning keras neural-networks parkinsons-disease python self-supervised-learning
Last synced: about 1 month ago
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Multi-shared-task Self-supervised Learning utilizing CNN-LSTM network
- Host: GitHub
- URL: https://github.com/mshuqair/multi-shared-task-self-supervised-cnn-lstm
- Owner: mshuqair
- Created: 2024-06-26T14:09:44.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-07-09T13:45:05.000Z (6 months ago)
- Last Synced: 2024-07-09T17:22:41.705Z (6 months ago)
- Topics: cnn-lstm, deep-learning, keras, neural-networks, parkinsons-disease, python, self-supervised-learning
- Language: Python
- Homepage:
- Size: 394 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Citation: CITATION.cff
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README
# Multi-shared-task Self-supervised (M-SSL) Multichannel CNN-LSTM
## M-SSL Multichannel CNN-LSTM for UPDRS-III Estimation in PD Patients
**Figure 1.** The main algorithm for estimating UPDRS-III scores.
 
**Figure 2.** The correlation of the Supervised (left) and the proposed M-SSL (right) Multichannel CNN-LSTM.
- This code is to estimate UPDRS-III scores of PD patients.
- The MDPI Bioengineering journal has recently published the research associated with this code.
- Multi-Shared-Task Self-Supervised CNN-LSTM for Monitoring Free-Body Movement UPDRS-III Using Wearable Sensors: https://doi.org/10.3390/bioengineering11070689
## General Note
- The code treats the estimation of UPDRS-III scores as a regression problem. If you want to use the model as a classifier, you need to alter the model's output layer and loss function.
- The code performs a leave-one-out subject-wise testing. You can replace the folds with the desired training and testing data.
- The original data is not available due to privacy concerns.
## Code Requirements and Compatability
The code was run and tested using the following:
- Python 3.10.11
- tensorflow 2.10.1
- keras 2.10.0
- h5py 3.10.0
- matplotlib 3.9.0
- numpy 1.26.3
- pandas 2.1.4
- scikit-learn 1.5.0
- scipy 1.13.1
- transforms3d 0.4.1
## Formating Your Own Data
If you want to use your data, consider the following:
- The shape of the input should be (n, 6), where n is the number of samples.
- The six columns are Gyroscope Wrist X, Y, Z and Ankle X, Y, Z.
- The labels are to be shaped as (n, 1).
- Modify 'data_generate_raw.py', 'data_generate_spectrogram.py', 'data_pretext_generate_raw.py', and 'data_pretext_generate_spectrogram.py' to remove any dependancy on orginal data.
## Running the Model
- Run 'data_generate_raw.py' and 'data_generate_spectrogram.py' to segment the Gyroscope Raw data and generate Spectrograms.
- Pretext Task: run 'task_pretext_multitask_dual.py' to pre-train the Multichannel CNN using the proposed Multi-shared-task Self-supervised Learning (M-SSL) approach.
- The code will generate the selected signal transformations and their spectrograms for the Pretext task.
- The pre-trained models will be saved in the 'models' folder
- Downstream Task: Run 'task_downstream_dual_cnn_lstm.py' to transfer the weights from the pre-trained models, fine-tune them and evaluate the testing data.
- Baseline task: run 'task_baseline_dual_cnn_lstm.py' to get the model performance in a Supervised scenario (not using M-SSL).
- Use any of the analysis codes to calculate metrics and visualize outputs.
## Conclusion