It has become common for people to experience stress, mainly because of its eclectic nature - physical, psychological, emotional, social, etc. Unmonitored stress may prove harmful to one's health resulting in even chronic diseases. In this study, we have demonstrated how electrodermal activity (EDA), which represents the sympathetic response to stress, could be used for accurate classification of stress by developing a machine learning based classification model.. The proposed kNN classifier has sensitivity and specificity of 94% and 93% respectively. Motion corruptions due to hand movements were detected using the accelerometer data and were classified as `motion affected'. The classifier was able to classify - the baseline regions of all participants as non-stress, 93% of the TSST regions as stress and 63% of the post-stress regions as non-stress.

The video describes about the person experiencing different levels of stress, which is replicated in the graph. The above work was published at MEMEA 2018