Smart Stress Monitoring and Personalized Music Recommendation Using IoT and Machine Learning

Abstract: Mental health has emerged as a prime area of concern over the last few years, particularly among the youth and corporate professionals. The current research formulates a framework that applies machine learning to label stress levels and then suggests music according to the age group of the user. Based on the analysis of physiological and survey-based inputs, the model classifies stress as Low, Moderate, and High and maps results against age for recommending personalized music. Methods like SMOTE-Tomek resampling, PCA, and Random Forest Classifier were applied for accuracy improvement. Results indicate high accuracy in stress classification and validate age-based music therapy as a complementary method for stress alleviation