Music Recommendation Model Based on Emotion Detection Using Pulse Rate and Stress Measurement

Music is a way of releasing stress and make a refreshing mood when the peoples are going through a hectic life style. Now a day’s there are many Music recommendation systems available based on collaborative or content-based recommendation engines. The choice of music not only based on the contents or previous preferences, the mood of a person at a specific time plays an important role for listening and enjoying a music. This paper proposes a music recommendation model based on emotion detection using pulse rate and stress measurement with sensors. The pulse rate is measured using mobile application from these data PPG can be plotted. Using the PPG graph the data is analyzed and histogram is plotted. The histogram helps to find the stress index. Using Pulse Rate and Stress Index in a logistic regression the emotion can be predicted. This predicted value is used for developing music recommendation model.