aceDesign and optimization of deep learning model based on multimodal data fusion for dynamic mental health assessment
The dynamic assessment of mental health has emerged as a hotspot for study and application due to the rise in social pressure. However, onventional methods rely mostly on static scales or single-modal data, failing to fully capture multifaceted emotional and behavioral features. This study suggests a deep learning model based on multi-modal data fusion to address this problem. By combining information from multiple sources, including text and visuals, the model effectively identifies and dynamically monitors men…
A Bi-LSTM and CNN fusion model combining text and visual data achieved 91.3% precision and 90.1% F-score on emotion recognition and up to 85.32% accuracy on depression classification, outperforming single-modal baselines.
Evidence
- Peer-reviewedBiomedical Physics & Engineering Express2026-08-06
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Truvace Impact Record TRV-2026-0678, v1: “Design and optimization of deep learning model based on multimodal data fusion for dynamic mental health assessment.” Truvace, 2026-08-07. /record/TRV-2026-0678 (accessed at citation time). sha256 4b79c05e0eab569d…
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