Vinoth Kumar.P1, Sijithamole.S2, Sowmiyadevi.V.M3, Rajkumar.E4, Vignesh.D.V5
1Assistant Professor, 2,3,4,5UG Students – Final Year, Department of Electronics and Communication Engineering, Nandha College of Technology, Perundurai, Tamilnadu, India
Nowadays the IOT system is widely used in various fields and has attached much attention in the field of healthcare. Emotional recognition is an important subject when talking about human mechanical communication and when analyzing and controlling emotions. Emotions can be detected using a variety of techniques, including facial expressions, speech, body language and psychological signals. Some physiological changes occur in the human body, for example changes in heart rate, temperature, skin conductivity, muscle tension and brain waves. This paper extracts temperature, blood pressure and heart rate values from a temperature sensor, pulse sensor and ECG sensor and uses them to teach in depth learning. Convolution neural network classifier algorithm, which assumes a classification problem based on the conditional probability model and that each aspect is independent of the other, the advantage of using this method is that each person has different patterns based on heart rate. Sometimes the normal body temperature and heart rate of individuals vary. The same database is used to train CNN, for example another classification problem that helps to analyze the algorithm, which is more accurate. In deep learning can help accurately predict each person’s emotional state.
Index Terms: Human Identification, emotional recognition, deep learning, health monitoring, IOT.