Biofeedback based computational approach for Working Stress reduction through meditation technique

Working stress is recognized as one of the major challenges faced by the working community around the world. Working Stress is the response of the people, when presented work demands are not matched to their knowledge and ability and which challenge their ability to cope. Increased stress at work cause depression and anxiety. Working stress cannot be avoided, so mechanism to cope with the stress positively should be identified and practiced. Mindful meditation together with biofeedback helps to reduce the working stress. Quantitative classification of mental stress is important to cope up with the stress effectively. This paper proposes a novel approach for detecting and classifying stress using galvanic skin response and image based biofeedback assisted mindfulness meditation to cope with the stress positively in real time. Stress was classified into two levels and meditation was identified as separate level using a statistical framework and during the evaluation of the framework proposed method obtained the accuracy of 82.418%, 73.945% and 64.96% for each respective level.