Stress Management Using Artificial Intelligence

The problem of stress is recognized as one of the major factors leading to a spectrum of health problems. Today the diagnosis and the decision is largely dependent on how experienced the clinician is interpreting the measurements. Computer aided artificial intelligence systems for diagnosis of stress would enable a more objective and consistent diagnosis and decisions. The stress-detection system is proposed based on physiological signals. Parameters like galvanic skin response (GSR), heart rate (HR), Body temperature, Muscle tension, Blood pressure are proposed to provide information on the state of mind of an individual, due to their non-intrusiveness and non invasiveness. The metamorphosis provided in this system is to improve the accuracy level in diagnosis. The response from the sensors reflects reaction of individuals and their body to stressful events. Some individuals may react differently to stressful events due to body condition, age, gender, experience and so on. Uncertainties and complexities exists that need to be dealt with while defining stress. Fuzzy Logic can overcome this. This result improves former approaches in literature and well-known machine learning techniques like SVM. k NN, GMM and Linear Discriminant analysis. Things are now no longer just black and white, but all the shades of grey in between as well. Half-truths are allowed and indeed encouraged. Our system combines the human-like reasoning style, learning and connectionist structure of the fuzzy system. The fluctuating stress parameters are processed using fuzzy logic. The strength of fuzzy systems involves two contradictory requirements interpretability versus accuracy. The innovative use of Fuzzy system in our project provides an optimum solution to abate the stress level of a person after performing multifarious analysis efficiently.