A supervised machine learning semantic segmentation approach for detecting artifacts in plethysmography signals from wearables

Step (1): 30-second signal segments from PPG DaLiA were split into train and test set by randomly chosen subject IDs.
Step (2): Datasets were uploaded into the web annotation tool for human annotation.
Steps (3): and (4): The tool transcribes human annotations into binary segmentation label masks (ground truth) and 30-second signal segments were stored into the back-end database.
Step (5): Ground truth and 30-second signal segments were then used to train the model.
Step (6): Predictions by the trained model are made on the PPG DaliA test set, WESAD, TROIKA, and UCSF datasets.
Step (7): The trained model’s predictions are provided for evaluation against human-annotated ground truth.

Use environment_no_builds.yml if not on Linux
	*Packages might be different on different OS, if conflicts exists, please delete the confilct packages from yml file, since some packages are required only on Ubuntu/ Linux

Place test set in data/ folder\
	* 	test set folder structure should look like this:
		TESTSETNAME
			|_ processed_dataset
				|_ scaled_ppgs.npy
				|_ seg_labels.npy
		 
		scaled_ppgs.npy has shape (n*1920)
		seg_labels.npy has shape (n*1920), consists of only 1 and 0 integers
		* files must be named scaled_ppgs.npy and seg_labels.npy

cd to model folders (cnn_slider, pulse_template_matching, resnet34, proposed)

Run evaluation with command: python run_evaluations.py TESTSETNAME plot_limiter
	- TESTSETNAME is the same name of the test set folder you placed in the parent data/ folder
	- plot_limiter is an integer, how many plots you want to generate for sampling purpose
	* generate plots will increase evaluation time dramatically, recommend 0 if evaluation is the priority