DATA ANALYSIS BY USING MACHINE LEARNING ALGORITHM ON CONTROLLER FOR ESTIMATING EMOTIONS

Abstract

Emotions are an unstoppable and uncontrollable aspect of mental state of human. Some bad situations give stress and leads to different sufferings. One can’t avoid situation but can have awareness when body feel stress or any other emotion. It becomes easy for doctors whose patient is not in condition to speak. In that case person’s physiological parameters are measured to decide emotional status. While experiencing different emotion, there are also physiological changes taking place in the human body, like variations in the heart rate (ECG/HRV), skin conductance (GSR), breathing rate(BR), blood volume pulse(BVP),brain waves (EEG), temperature and muscle tension. These were some of the metrics to sense emotive coefficient. This research paper objective is to design and develop a portable, cost effective and low power embedded system that can predict different emotions by using Naive Bayes classifiers which are based on probability models that incorporate class conditional independence assumptions. Inputs to this system are various physiological signals and are extracted by using different sensors. Portable microcontroller used in this embedded system is MSP430F2013 to automatically monitor the level of stress in computer. This paper reports on the hardware and software instrumentation development and signal processing approach used to detect the stress level of a subject.To check the device's performance, few experiments were done in which 20 adults (ten women and ten men) who completed different tests requiring a certain degree of effort, such as showing facing intense interviews in office.