Study on the path of combining music and digital health technology to promote the health of older adult groups

Objective: As the global population ages, non-pharmacological interventions such as personalized music therapy show promise for wellbeing in older adults. We propose the Fusion-Attentive Temporal Network (FAT-Net). This dual-stream model processes minute level heart-rate and music on/off data alongside daily summary features to predict a composite health score. Methods: Data from 92 participants over 45 ± 10 days were augmented fourfold using jittering, time-warping, magnitude scaling, and SMOTE. The temporal stream uses Conv1D, BiLSTM, and self-attention pooling. The summary stream uses a three-layer MLP. Cross-modal attention fuses both embeddings. Results: between predictions and true values was 0.93. Conclusion: FAT-Net's attention-based fusion provides a robust, interpretable approach for forecasting daily wellbeing in older adults.