Information-Fusion-Lab-Umass / personalized-stress-prediction

Dynamic clustering via branched deep learning enhances personalization of stress prediction from mobile sensor data

Student Mental Health

This is a repository for the paper titled "Dynamic clustering via branched deep learning enhances personalization of stress prediction from mobile sensor data" at Nature Scientific Report.

Relevant Resources

• StudentLife study.
• Extensive literature review on other psychology related papers for domain knowledge.
• Personalized Student Stress Prediction with Deep Multitask Network

Owners/Authors

• Yunfei Luo ([e-mail], [e-mail])
• Iman Deznaby ([e-mail])
• Abhinav Shaw ([e-mail])
• Natcha Simsiri ([e-mail])
• Dr. Tauhidur Rahman ([e-mail])
• Dr. Madalina Fiterau ([e-mail])

Input format

For each sample, the data needed are:

• ID of the subject that this sample belongs to (format: id_month_day_time)
• Time-series data (or features vector).
• Covariate data (features vector).
• Pre-processed data (e.g. histogram with 1 minute bins).
• Label.

For grouping, this have to be done before training or inference:

• Clustering the subjects based on chosen criteria (e.g. survey scores, DTW, or any relevant information).
• Saved the result clusters as a dictionary in the format of "map: subject_id -> group_id".
• If it is personalized model, then the group mapping should be one unique group_id for each subject_id.

A sample fake data in sample_input.py indicate the desired format.

Training and Output

For training and validation:

• Currently support 5-fold and leave-one-subject-out validation.
• If custom train/val split is available, it can be added at the "get_splits" function in src/utils/train_val_utils.py.
• the main training function is the "train_and_val" function in src/utils/train_val_utils.py.
• The hyper-parameters are set under src/experiments/config.py.

Training command:  
python3 -m src.experiments.run_exp job_session_name validation_type num_branches days_include cluster_name remark

Example:
Baseline: python3 -m src.experiments.repeat_exp lstm 5fold 1 0 all_in_one baseline
Calm-Net: python3 -m src.experiments.repeat_exp calm_net 5fold 1 0 one_for_each calm_net
Branching Calm-Net: python3 -m src.experiments.repeat_exp calm_net 5fold 3 0 one_for_each branching
Transformer example: python3 -m src.experiments.repeat_exp trans_calm_net_with_branching 5fold 1 0 one_for_each trans_calm_net_with_branching

LOOCV example with 1 week (7 days) included: python3 -m src.experiments.run_exp calm_net loocv 3 7 one_for_each branching

For experiments on WESAD:

• cd to folder wesad/
• Run command with example:

python3 -m main personalize loocv

Reference: The preprocessing pipeline for WESAD is owned by: https://github.com/Emognition/dl-4-tsc

For inference:

• The training function train_and_val function in src/utils/train_val_utils.py will return all the outputs including those from samples in validation set.
• The output and evaluation scores are saved under data/cross_val_scores/
• include keyword "test_only" in the job_session_name, and set the validation_type also to "test_only".