Deep Learning-Based Detection of Obsessive-Compulsive Hand Washing

Description

This repository contains the code and resources for the Master Thesis titled, "Deep Learning-Based Detection of Obsessive-Compulsive Hand Washing". The project aims to develop an automated system using deep learning methods to detect instances of enacted obsessive-compulsive hand washing behavior.

The Methodology of this paper is as follows:

Tasks Types:

• Task 1: Null vs. HW (HandWashing)
• Task 2: rHW (Routine Handwashing) vs. cHW (Compulsive Handwashing)
• Task 3: Null vs. rHW vs. rHW
• Task 4: Null vs. cHW
• Task 5: DL model personalization

Installation:

The project requires a Linux system that is equipped with Cuda 11.7.

All subsequent commands assume that you have cloned the repository in your terminal and navigated to its location.

A file named "env.yml" contains all necessary python dependencies.

To conveniently install them automatically with anaconda you can use:

conda env create -f env.yml

conda activate ml23

Data

To replicate our experiments, please download the corresponding processed data for DeepLearning experiments and feature data for ML experiments. Extract these zip files into the data folder.

Config files

The folder configs consists of different configurations for different tasks which names must be passed as an argument for targeted task. As per the thesis work, following configs files are as indicated in the file_name

Reconstruction

$ python main.py configs/[config_file_name] --m [method] --t [task_type] --d 

Data prepration

• To prepare dataset before running pass flag --d
• The original datset OCDetect_Export by Burchard et al. must be inside data folder.
• If not passed, the pre-existed data will be used.

Run DL Tasks:

• Pass --d to pre-process before start training

$ python main.py configs/task1_adam.yaml --m dl
$ python main.py configs/task1_sgd.yaml --m dl

$ python main.py configs/task2_adam.yaml --m dl
$ python main.py configs/task2_sgd.yaml --m dl

$ python main.py configs/task3_adam.yaml --m dl
$ python main.py configs/task3_sgd.yaml --m dl

$ python main.py configs/task4_adam.yaml --m dl
$ python main.py configs/task4_sgd.yaml --m dl

$ python main.py configs/task5_adam.yaml --m dl
$ python main.py configs/task5_sgd.yaml --m dl

None: To run Task-5, Task-2 must be completed so that the best models will be saved in best_model folder. None: To run Task-5, Task-2 must be completed so that the best models will be saved in best_model folder.

Run ML Tasks:

• Pass --d to pre-process before start training

$ python main.py configs/task1_adam.yaml --m ml
$ python main.py configs/task1_sgd.yaml --m ml

$ python main.py configs/task2_adam.yaml --m ml
$ python main.py configs/task2_sgd.yaml --m ml

$ python main.py configs/task3_adam.yaml --m ml
$ python main.py configs/task3_sgd.yaml --m ml

$ python main.py configs/task4_adam.yaml --m ml
$ python main.py configs/task4_sgd.yaml --m ml

None: To run Task-5, Task-2 must be completed so that the best models will be saved in best_model folder. None: To run Task-5, Task-2 must be completed so that the best models will be saved in best_model folder.

Folder Structures

The 'saved' folder consists of folders where the results will be stored.

• Logs are stored in logs folder
• Charts are stored in charts folder
• Models are stored in models folder
• Results are stored in results folder

├── saved
│   ├── 2024-01-24..
│   ├     ├── charts
│   ├     ├── logs
│   ├     ├── models
│   ├     ├── results
│   ├           ├── 2024-01-24.csv
├── src
└── .gitignore

Running in OMNI-Cluster (University of Siegen)

The jobscripts are stored inside jobscripts folder

Run DL pipeline Task 1 to Task 4

$ bash run_dl.sh

Run ML pipeline Task 4 to Task 4

$ bash run_ml.sh

Run Personalization after completion of DL pipeline

$ bash run_personalized.sh

Results

Data visualization

Task 2: rHW vs cHW

Task 5: DL Personalization [rHW vs cHW]

Contact

For questions and comments please contact Amir Thapa Magar via mail