Multimodal Data Extraction & Fusion for Health Monitoring System and Early Diagnosis
Kulvinder Singh, Piyush Piyush, Ritik Kumar, Sonal Chhabra, Nidhip Goomer, Aanchal Kashyap · 2024
Multimodal data fusion involves combining varied data types to enhance analysis, yielding comprehensive insights and improving decision-making in diverse applications. Current literature tends to focus on individual aspects, lacking a holistic perspective. A lack of standardized frameworks for effective multimodal fusion hampers seamless integration. Bridging these gaps is imperative to unlock the full potential of multimodal data fusion, ensuring more accurate diagnostics, personalized interventions, and improved patient outcomes. This research paper delves into Multimodal Data Fusion for Health Monitoring, acknowledging the significant strides made in the field while highlighting pertinent research gaps. The study emphasizes the need for a comprehensive understanding of the dynamic interactions between various data modalities and their impact on healthcare outcomes. This paper identifies these critical research gaps and serves as a guide for future investigations, steering the field toward a more robust and integrated approach to health monitoring.