The metaverse enables immersive virtual healthcare environments,presenting opportunities for enhanced care delivery.A key challenge lies in effectively combining multimodal healthcare data and generative artificial in...The metaverse enables immersive virtual healthcare environments,presenting opportunities for enhanced care delivery.A key challenge lies in effectively combining multimodal healthcare data and generative artificial intelligence abilities within metaverse-based healthcare applications,which is a problem that needs to be addressed.This paper proposes a novel multimodal learning framework for metaverse healthcare,MMLMH,based on collaborative intra-and intersample representation and adaptive fusion.Our framework introduces a collaborative representation learning approach that captures shared and modality-specific features across text,audio,and visual health data.By combining modality-specific and shared encoders with carefully formulated intrasample and intersample collaboration mechanisms,MMLMH achieves superior feature representation for complex health assessments.The framework’s adaptive fusion approach,utilizing attention mechanisms and gated neural networks,demonstrates robust performance across varying noise levels and data quality conditions.Experiments on metaverse healthcare datasets demonstrate MMLMH’s superior performance over baseline methods across multiple evaluation metrics.Longitudinal studies and visualization further illustrate MMLMH’s adaptability to evolving virtual environments and balanced performance across diagnostic accuracy,patient-system interaction efficacy,and data integration complexity.The proposed framework has a unique advantage in that a similar level of performance is maintained across various patient populations and virtual avatars,which could lead to greater personalization of healthcare experiences in the metaverse.MMLMH’s successful functioning in such complicated circumstances suggests that it can combine and process information streams from several sources.They can be successfully utilized in next-generation healthcare delivery through virtual reality.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.62202247the National Key R&D Program of China under Grant No.2022ZD0115303.
文摘The metaverse enables immersive virtual healthcare environments,presenting opportunities for enhanced care delivery.A key challenge lies in effectively combining multimodal healthcare data and generative artificial intelligence abilities within metaverse-based healthcare applications,which is a problem that needs to be addressed.This paper proposes a novel multimodal learning framework for metaverse healthcare,MMLMH,based on collaborative intra-and intersample representation and adaptive fusion.Our framework introduces a collaborative representation learning approach that captures shared and modality-specific features across text,audio,and visual health data.By combining modality-specific and shared encoders with carefully formulated intrasample and intersample collaboration mechanisms,MMLMH achieves superior feature representation for complex health assessments.The framework’s adaptive fusion approach,utilizing attention mechanisms and gated neural networks,demonstrates robust performance across varying noise levels and data quality conditions.Experiments on metaverse healthcare datasets demonstrate MMLMH’s superior performance over baseline methods across multiple evaluation metrics.Longitudinal studies and visualization further illustrate MMLMH’s adaptability to evolving virtual environments and balanced performance across diagnostic accuracy,patient-system interaction efficacy,and data integration complexity.The proposed framework has a unique advantage in that a similar level of performance is maintained across various patient populations and virtual avatars,which could lead to greater personalization of healthcare experiences in the metaverse.MMLMH’s successful functioning in such complicated circumstances suggests that it can combine and process information streams from several sources.They can be successfully utilized in next-generation healthcare delivery through virtual reality.
