Multimodal Metaverse Healthcare: A Collaborative Representation and Adaptive Fusion Approach for Generative...

Area of Science:

• Artificial Intelligence in Healthcare
• Virtual Reality and Digital Health
• Multimodal Data Fusion

Background:

• The metaverse offers immersive virtual healthcare environments for improved care delivery.
• Integrating multimodal health data (text, audio, visual) with AI in the metaverse presents significant challenges.
• Existing methods struggle with effective data fusion and representation in complex virtual healthcare settings.

Purpose of the Study:

• To propose a novel multimodal learning framework for metaverse healthcare applications.
• To address the challenge of effectively combining diverse healthcare data within virtual environments.
• To enhance feature representation and data fusion for complex health assessments in the metaverse.

Main Methods:

• Developed a multimodal learning framework (MMLMH) based on collaborative intra- and inter-sample representation and adaptive fusion.
• Employed modality-specific and shared encoders with intrasample and intersample collaboration mechanisms.
• Utilized attention mechanisms and gated neural networks for adaptive fusion of multimodal data.

Main Results:

• MMLMH demonstrated superior feature representation for complex health assessments compared to baseline methods.
• The adaptive fusion approach showed robust performance across varying data quality and noise levels.
• Experiments on metaverse healthcare datasets confirmed MMLMH's superior performance across multiple evaluation metrics.

Conclusions:

• MMLMH effectively combines and processes information streams from multiple sources in metaverse healthcare.
• The framework exhibits adaptability to evolving virtual environments and maintains balanced performance.
• MMLMH offers enhanced personalization and can be utilized for next-generation virtual reality healthcare delivery.