Develop a personalized intelligent music selection system based on heart rate variability and machine learning

Music often plays an important role in people’s daily lives. Because it has the power to affect human emotion, music has gained a place in work environments and in sports training as a way to enhance the performance of particular tasks. Studies have shown that office workers perform certain jobs better and joggers run longer distances when listening to music. However, a personalized music system which can automatically recommend songs according to user’s physiological response remains absent. Therefore, this study aims to establish an intelligent music selection system for individual users to enhance their learning performance. We first created an emotional music database using data analytics classifications. During testing, innovative wearable sensing devices were used to detect heart rate variability (HRV) in experiments, which subsequently guided music selection. User emotions were then analyzed and appropriate songs were selected by using the proposed application software (App). Machine learning was used to record user preference, ensuring accurate and precise classification. Significant results generated through experimental validation indicate that this system generates high satisfaction levels, does not increase mental workload, and improves users’ performance. Under the trend of the Internet of Things (IoT) and the continuing development of wearable devices, the proposed system could stimulate innovative applications for smart factory, home, and health care.