Physiological Response Patterns from Wearable Sensors During Stress and Exercise Conditions

The ever-growing enablement of wearable sensing technologies in everyday lives has presented radical possibilities to real-time physiological monitoring of humans. This research examines the trends of physiological responses obtained by wearable devices in stress and exercise scenarios and how they are likely to demonstrate correlations between psychological and physical reactions to stressors. This paper presents dynamic response features and their commonalities between mental stress and physical exertion using real-time biometric parameters collected using heart rate variability (HRV), galvanic skin response (GSR) and skin temperature. The scope has two goals: to answer which is the investigation of the correlation between physical exertion and psychological stress, and the second is to set the grounds on which the future areas of use of Machine Learning (ML) and signal processing would be utilised on the basis of early detection of stress and health data analytics in the Indian community. It is hoped that the results will lead to adaptive ML schemes of wearable gadgets that will be able to determine which load is cognitive and which one is physical, thus improving the preventive medical practise and the optimization of performance.