Communications in Humanities Research ISSN: 2753-7072
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Research Article

A Study of Emotion-Aware Personalized Music Therapy and Adaptive Recommendation Systems

Macheng Cao*
Shenzhen College of International Education

* Corresponding author: [email protected]

Abstract

Existing studies indicate that music therapy has a positive effect on insomnia. However, the effectiveness of traditional music therapy is often limited by subjective evaluation and practical barriers. Therefore, it is necessary to provide a more reproducible, accessible home-based conceptual method to relieve insomnia. The well-established machine learning model will become the core of it. Instead of using therapists' subjective evaluation, objective data such as electroencephalogram (EEG), Electrocardiogram (ECG) and Photoplethysmography (PPG) are suitable to provide more consistent judgements. Additionally, the recent more reliable wearable EEG devices lay a favorable foundation for this concept. Methods of quantification of arousal (both sleep-related and emotional arousal) have been introduced, so that the time-series signals, after noises being filtered or avoided, are input to Long Short-Term Memory (LSTM) network continuously. After the initialization is finished with rule-based program, it will collect users' emotional feedback and predict users' reaction to music based on previous record of their feedback to the recommended music. It aims at improving sleep quality consequently. Reinforcement Learning (RL) will also be integrated to enable the algorithm adapt to users' preference repetitively after investigating the feedbacks.

Keywords: Music Therapy; Insomnia; Machine Learning
Published: September 15, 2026
DOI: 10.54254/2753-7064/2026.BJ36728
Volume: CHR Vol.119
pp. 78-83
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