Rui WangThis email address is being protected from spambots. You need JavaScript enabled to view it.
School of Culture and Media, Yantai University of Science and Technology, Penglai, Shandong, 265600, China
Received: October 25, 2025 Accepted: December 12, 2025 Publication Date: March 27, 2026
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
The rapid development of digital media art (DMA) has created a demand for intelligent systems that can understand and respond to users via numerous sensory channels. However, current systems frequently rely on single-modal inputs, which limit their ability to understand user states and adjust experiences accordingly. This research seeks to create an intelligent DMA system architecture that combines multimodal perception to enhance user experience dynamically. A novel Dolphin Swarm Optimized Deep Neural Network (DSO DeepNet) is proposed, where DSO dynamically tunes hyperparameters of the DeepNet to maximize multimodal emotion recognition performance, leading to improved user experience adaptation in DMA systems. The system combines physiological inputs (heart rate variability (HRV), electrodermal activity (EDA)), visual data (facial ex pressions), and audio cues (speech tone). A customized dataset was created from 250 individuals who interacted with digital artworks, recording synchronized biometric, visual, and audio data. Pre-processing techniques include signal denoising with band-pass filters and Wiener filtering. For feature extraction, Mel Frequency Cepstral Coefficients (MFCCs) and ResNet50 are used. Deep Canonical Correlation Analysis (DCCA) is recommended for effective fusion, alignment, and integration of information from multiple modalities. Experimental results showed that the fused multimodal model outperformed the other models in terms of emotion recognition accuracy (0.925). Emotional involvement, interpersonal satisfaction, and perceived system responsiveness all improved significantly, according to user experience evaluations. Overall, the proposed architecture effectively improves the sensitivity and adaptability of DMA systems through multimodal perception fusion, indicating a promising route for producing more immersive and individualized art experiences.
Keywords: Digital media art (DMA) system, user experience, emotion recognition, Dolphin Swarm Optimized Deep Neural Network (DSO-DeepNet).
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