Public protection practitioners, including LEAs, are forced to make difficult decisions during high-pressure operations, under strenuous circumstances. Systems and applications that assist such practitioners to take decisions, are increasingly incorporating user stress level information for their development, adaptation, and evaluation, as human stress is intricately linked with mental processes such as decision making.

In this paper, a method to accurately detect and classify the level of acute, short-term stress, in real time, for the development of personalized, context-aware solutions for LEAs is proposed, by utilising two Convolutional Neural Network architectures for the stress detection and 3-level (low, moderate, high) stress classification tasks, using ultra short-term raw electrocardiogram signals. The models were trained on the two publicly available stress classification datasets, after applying pre-processing techniques, such as data pruning, down-sampling, and data augmentation, using a sliding window approach. After hyperparameter tuning, using 4-fold cross-validation, the evaluation on the test set demonstrated state-of-the-art accuracy both on the 3- and 2-level stress classification task using the DriveDB dataset, reporting an accuracy of 83.55% and 98.77% respectively.

Read the full paper here.