Prediction and Detection of Stress using IoT and Supervised Learning Approach
Stress is the common reaction to many worse situations. High stress will leads to physiological and behavioral change. Continuous stress rate affects the human body in different aspects. This work aims to alert the individuals about their stress level through IoT and machine learning algorithms. This proposed work continuously monitors the heart rate of a person through the IoT devices since it is difficult for a cardiac surgeon to predict a person’s age from pulse since both are nonlinear. The heartbeat of a person varies depending on various factors also the pulse rate of an over trained person, athlete, obese; less exercising person varies from person to person. This work monitors a person heartbeat and continuously stores the values in the cloud, the cloud in turn depends on the machine learning training algorithms predicts the stress rate of a person and alarms the individual about their unhealthy condition. The approved individual can sign in, see the report and take activities, for example, counseling a clinical individual, play out some reflection or yoga activities to adapt to the condition.