Cyberintervention on plant workforce’s mental activity for safety

Stress is recognized as an important health and safety indicator in work environments as it can both endanger workers and hinder companies’ workflow. HRV is recognized as a good psychophysiological indicator of personal stress and can also be detected with innovative wearable electrocardiogram (ECG) bands which allow us to obtain recordings in real-life situations. This work proposes an innovative procedure for the assessment and a subsequent intervention against stress, using an AI approach for the detection of unhealthy stress status followed by a VR heart rate variability biofeedback treatment to address it. The procedure consists of assessing personal data and stress and tiredness levels of workers, and then collecting their ECG data through the cardio band Zephyr BioHarness during a standard workday. Researchers will shadow the participants without interfering, labeling each activity according to a predefined scale in clusters of homogeneous behaviors. After preliminary analysis, the data will populate a database to be used to train an AI with the goal to detect patterns related to stress and find out which HRV components are best at predicting stress. To compare our on-field recordings, we will also use data from open-source databases, with physiological registration of stressful situations. This procedure was tested on 11 plant workers during a standard job day.