Detection of Emotions and mood using IoT, Android and Machine Learning

Abstract

Understanding human thoughts since times always remained a mysterious challenge for the scientific discipline as is the circumstance with the emotions of human beings. The numerous techniques of Emotion detection has already been discovered, one among which we here have discovered is the detection of emotion which is here done using internet of things (IoT) and Machine learning technique. This paper is being proposed to present the scheme and execution of a emotion detection application, that has been calculate to detect the emotion and mood of a person for examining the triad physical constraints (temperature, pulsate, motion and skin electro-conductance) by means of algorithm that is related machine learning that is being trained with data set provided by an application called to be a mood detector. This application is tested redundantly unless the result which is produced by a learning algorithm that is confirmed to 100%, as a consequence affirming that the algorithms of machine learning offers the accurate results. An application coordinates a recommendation of music framework that recommends the client to pin their ears back to the vague music, which has been created to the recognized emotion. In this paper, we design a probabilistic data collection mechanism and on the collected data we perform a correspondence analysis. Finally we design a statistical model to anticipate the human temperament and recommend a music playlist in accordance with their current temperament.