Objective: Work stress is identified as the 'health epidemic of 21st century' by WHO because, when left unchecked, it wreaks havoc on human mind and body by accelerating the onset and progression of several health disorders. Hence, the evolution of strategies for early detection of mental stress is pivotal. The study presented here is one step towards the goal of developing a physiological parameter based psychological stress detection scheme which can further be incorporated into a wearable vital signs monitor. Approach: A group of 34 subjects (14 females and 20 males, age: 21.4 ± 1.7 years; mean ± SD) volunteered to participate in a pilot laboratory intervention that emulated real-life job stress scenarios by incorporating stress factors like mental workload, time pressure, performance pressure and social evaluative threat. Electrodermal Activity (EDA), Electrocardiogram (ECG), and Skin Temperature (ST) were monitored throughout the experiment to capture sympathetic activation during stress. Stress response elicitation was validated using salivary cortisol levels. A total of 61 features were extracted from these signals and four classifiers were investigated regarding their ability to detect 'stress' using single and multimodal schemes. A fusion framework that combined the benefits of feature fusion and decision fusion was employed to generate classifier ensembles for multimodal stress detection schemes. As the generated datasets exhibited a class imbalance issue, three separate schemes for class imbalance rectification viz., undersampling, oversampling and SMOTE were investigated concerning their ability to yield the best classification performance. While ECG based performance analysis was restricted to data segments of 300 s duration to conform to international guidelines for short-term HRV analysis, non-overlapping EDA and ST data segments of durations 300 s, 180 s, 60 s, and 30 s were examined to determine the optimum data length that can generate best results. Main Results: EDA gave a superior performance for 60 s windows while ST performed best with data segments of duration 30 s. A comparative study was performed with 25%, 50%, 75% and 90% overlapping data segments as well. However, overlapping did not enhance the performance of the classifiers significantly.While EDA emerged as the best single modality, the highest stress recognition accuracy of 97.13% was yielded by a combination of EDA and ST with data segments of 60 s duration. Furthermore, the differential effect of 'physical' and 'psychological' stressors on EDA and ST was analyzed. Significance: The results clearly suggest that these physiological parameters can not only reliably detect psychological stress but can also discriminate it from physical stress.

Stress being labelled by WHO as "the health epidemic of 21 century" need to be treated as a clarion call for devising strategies that aim at its early detection, for the reason that stress is the cause as well as the catalyst for several chronic human health disorders. The work reported here in is a progression towards the development of a stress detection system based on the electrodermal activity (EDA) in humans, which can further be incorporated into a wearable vital signs monitor. The utility of EDA as a potential physiological measure for classifying physical and psychological stressors is analyzed in this paper. A group of 12 subjects (8 males and 4 females, age: 25.4 ± 3.1 years, mean ± SD) volunteered to participate in a laboratory stress task that included a psychological stressor close to real life work stress scenario and a physical stressor. The capability of stressors to elicit persistent stress response was validated by assessing variations in salivary cortisol levels. EDA was monitored throughout the experiment sessions as a measure of sympathetic activation in subjects. Six classification models were investigated concerning their usability to distinguish physical and psychological stressors based on EDA. A maximum accuracy of 95.1% was achieved using linear discriminat analysis (LDA) based classifier which imply that EDA is indeed a potential discriminate measure to classify physical and psychological stress responses. Furthermore, the best feature combination for maximum classification accuracy was also determined.

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