Real-Time Adaptive Personalized Music Therapy in Mental Health Wellness with IoT and Artificial Neural Network

作者：Muhammad Saleem, Zeeshan Ahmed Mohammed, A Saranyadurai, C. Chandravathi, Angelin Blessy J, V E Sathishkumar · 年份：2025 · DOI：10.1109/iccsp64183.2025.11089430 · 被引用次数：1 · 研究领域：EEG and Brain-Computer Interfaces、Digital Mental Health Interventions、Emotion and Mood Recognition

In recent years, tailored therapies for mental health have gained popularity. The paper provides a novel framework for real-time adaptive individualized music treatment using Internet of Things (IoT) and Artificial Neural Networks (ANNs). The framework uses biometric sensors, data analytics, and music intervention methodologies to personalize treatments to physiological reactions to reduce stress and anxiety and improve mental health. The architecture starts with IoT-enabled biometric sensors that capture real-time heart rate, skin conductivity, and brainwave patterns. The main data source is these sensors, tracking physiological reactions in different situations. Data is sent to a central processing unit or cloud platform for analysis. In analysis, advanced artificial neural network models understand complex physiological-mental state relationships. The ANN model is trained using biometric data and mental health-boosting music therapy. Iterative training and optimization help the ANN model predict the optimal music therapy treatments using real-time physiological responses. Participants should assess planned music therapy sessions to improve the ANN model. A feedback and model improvement loop adapts the framework to new mental health needs.