Analysis of the intrinsic biophysical and molecular process correlations between music activity and biosensor-monitored mental health status

Music activities, such as listening or performing music, have been linked to various mental health benefits, including stress reduction, emotional regulation, and overall well-being. However, from the perspective of cellular and molecular biomechanics, the underlying mechanisms linking music activity and mental health status, especially as monitored through biosensors, require further exploration. With advancements in biosensor technology, it is possible to observe physiological indicators such as heart rate, galvanic skin response, and brainwave activity that reflect mental health in real time during music-related activities. Changes in heart rate might be associated with the modulation of autonomic nervous system activity, which in turn can affect the release of neurotransmitters and intracellular signaling pathways at the cellular and molecular level. Brainwave activity alterations could reflect changes in neural cell excitability and synaptic transmission, involving complex molecular cascades. The study examines the relationship between music activities and mental health status utilizing biosensor data to measure physiological responses associated with mental health indicators. Data preprocessing included normalization to standardize physiological measurements and noise reduction to enhance signal quality. Feature extraction utilized Scale-Invariant Feature Transform (SIFT) to identify key features associated with physiological changes during music engagement. The study proposed a Golden Jackal Optimized Intelligent Extreme Gradient Boosting (GJO-IXGBoost) method, which was then applied to analyze the processed data, providing robust insights into the correlations between music activities and mental health. Statistical techniques, including correlation analysis and regression modeling, were used in this study. The proposed method is the performance of various evaluation metrics such as MSE (9.8%), RMSE (21.3%), MAE (13.2%), accuracy (92%), F1-score (90.2%), sensitivity (91%), MCC (85.6%) and specificity (93%). The results suggesting a positive impact of music activities, especially active participation, on mental health as monitored by biosensor data could potentially be due to the modulation of cellular and molecular processes. Applying the GJO-IXGBoost method helps in deciphering these complex cellular and molecular biomechanical correlates, contributing to the evidence base for the therapeutic potential of music in mental health interventions at the cellular and molecular level.