A real-time stress classification system based on arousal analysis of the nervous system by an F-state machine.

Background And Objective: Detection and labelling of an increment in the human stress level is a contribution focused principally on improving the quality of life of people. This work is aimed to develop a biophysical real-time stress identification and classification system, analysing two noninvasive signals, the galvanic skin response and the heart rate variability.

Methods: An experimental procedure was designed and configured in order to elicit a stressful situation that is similar to those found in real cases. A total of 166 subjects participated in this experimental stage. The set of registered signals of each subject was considered as one experiment. A preliminary qualitative analysis of the signals collected was made, based on previous counselling received from neurophysiologists and psychologists. This study revealed a relationship between changes in the temporal signals and the induced stress states in each subject. To identify and classify such states, a subsequent quantitative analysis was performed in order to determine specific numerical information related to the above mentioned relationship. This second analysis gives the particular details to design the finally proposed classification algorithm, based on a Finite State Machine.

Results: The proposed system is able to classify the detected stress stages at three levels: low, medium, and high. Furthermore, the system identifies persistent stress situations or momentary alerts, depending on the subject's arousal. The system reaches an F score of 0.984 in the case of high level, an F score of 0.970 for medium level, and an F score of 0.943 for low level.

Conclusion: The resulting system is able to detect and classify different stress stages only based on two non invasive signals. These signals can be collected in people during their monitoring and be processed in a real-time sense, as the system can be previously preconfigured. Therefore, it could easily be implemented in a wearable prototype that could be worn by end users without feeling to be monitored. Besides, due to its low computational, the computation of the signals slopes is easy to do and its deployment in real-time applications is feasible.