Validating physiological stress detection model using cortisol as stress bio marker

In this work, we have presented the validation of a stress detection model using cortisol as the stress biomarker. The proposed model uses two physiological signals: Galvanic Skin Response (GSR) and Photoplethysmograph (PPG) to classify stress into two levels. GSR and PPG signals were collected from a total of 13 participants along with saliva samples taken at time points throughout the duration of the experiment. We have used 10 out of the 13 participants to train our model. Data from the remaining 3 participants was used to test the robustness of the model in distinguishing stressed states from non-stressed states. We have achieved an overall accuracy of 92% with the model achieving precision, recall and f1-score of 93%, 99% and 96% respectively in predicting the occurrences of stressful events. Results indicate the promise of the proposed methodology in accurately detecting the presence of stressful events by generalizing the test data coming from a subset of population in contrast to the training data.