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.

Introduction

Overview:
Mental health concerns such as stress, anxiety, and depression are on the rise globally. While conventional treatments exist, there is increasing interest in non-pharmacological approaches like music therapy. This study presents an IoT-integrated system that uses physiological monitoring and machine learning (ML) to assess stress levels before and after music therapy, offering personalized, age-based music interventions.

Key Components:

1. Sensors & Data Collection:

• Heart Rate Sensor (e.g., MAX30100/MAX30102) and GSR Sensor are used to capture real-time physiological signals.

• Data is collected before and after a music therapy session.

• Signals indicate autonomic nervous system activity, which correlates with stress.

2. Data Processing & Machine Learning:

• Preprocessing steps include Label Encoding, Standardization, and Principal Component Analysis (PCA) for dimensionality reduction.

• SMOTE-Tomek is applied for class balancing.

• A Random Forest Classifier is trained to classify stress into Low, Moderate, and High.

• Performance is evaluated via precision, recall, F1-score, and confusion matrices.

3. Personalization:

• Music is recommended based on user's age group:

  • Example: Lo-fi or 2000s pop for 20–29 age group; retro hits for 50+.

• The system provides visualizations to show the impact of therapy.

Literature Review Highlights:

• Multiple studies validate the use of IoT-based monitoring and music therapy for stress and hypertension.

• Adaptive music systems using HRV, GSR, and AI-powered analytics show strong results.

• Personalized, feedback-driven music therapy can positively impact mental well-being and cardiovascular health.

Results:

Model Performance:

• Best recall in the High stress class, showing effective identification of acute stress.

• Some overlap between Low and Moderate due to physiological signal similarity.

Age-Based Stress Trends:

• Under 20 and 50+ groups exhibit the highest stress levels, justifying the need for age-specific therapeutic approaches.

Conclusion

This paper introduces a new, comprehensive method of stress classification employing IoT-integrated sensors and ML models. Through the use of real-time physiological signals and tailoring interventions for age and preference, we develop a system not just diagnosing but also reducing stress. Major conclusions are: 1) GSR and Heart Rate sensors are reliable predictors of real-time stress. 2) Random Forest classifiers, following appropriate preprocessing and dimensionality reduction, can accurately classify stress levels. 3) Personalized music therapy demonstrates real benefits, particularly for younger users. 4) Visualization helps to make the model interpretable and actionable for both users and mental health professionals. Future work can explore integration with mobile applications, continuous monitoring with wearable devices, and expansion to multi-sensor data (like EEG, respiration rate) for even more accurate emotional health tracking.

References

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Copyright

Copyright © 2025 Varad U. Deshpande, Rushikesh Tapsale, Dheeraj Borse, Jay Gajare. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.