Exploration of piano performers’ music performance status and psychological health assessment based on data mining and biomechanics

The mental, physical, and social features of health are all essential for overall comfort. It impacts the way individuals’ behavior themselves and works in day-to-day life, which affects their capacity to manage stress, communicate with someone else, and make decisions. For pianists, supporting high performance status entails not just technological mastery but also the capacity to handle physical and mental stresses. The study aims to evaluate the connection between the music performance level of pianists and psychological health based on novel data mining and biomechanics. In this study, a novel Drosophila Food Search refined support vector machine (DFS-RSVM) is proposed to evaluate the psychological health of piano performers. Psychological health factors, including stress, anxiety, and mental health focus, are related to piano musical performance. The study collected biomechanical data and sensor data correlated to posture, hand activities, muscle activity, and finger force. The data was preprocessed, utilizing normalization for the gathered data. Discrete wavelet transforms were applied to the features extracted from the dataset. The outcomes exposed the important relations between biomechanical stressors throughout piano performance and mental health, emphasizing the need for a suspicious strategy for mental health concerns. The results demonstrated the DFS-RSVM achieved superior performance and enhanced the health and performance of musicians, contributing to improved support for their physical and psychological well-being compared to other existing algorithms.