ArticlePloS one2025
MAF-Net: A multimodal data fusion approach for human action recognition.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Bio-Inspired Adaptive Multimodal Decision Fusion for Intelligent Safety Monitoring in Confined Spaces.Biomimetics (Basel, Switzerland) · 2026Article
- Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models.Sensors (Basel, Switzerland) · 2026Article
- A dataset of midpalatal suture maturation stage in cone-beam computed tomography.Scientific data · 2026Article
- Article
- Dynamic graph neural networks for UAV-based group activity recognition in structured team sports.Frontiers in neurorobotics · 2025Article
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Authors and funding
5 authors.
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Abstract
3D skeleton-based human activity recognition has gained significant attention due to its robustness against variations in background, lighting, and viewpoints. However, challenges remain in effectively capturing spatiotemporal dynamics and integrating complementary information from multiple data modalities, such as RGB video and skeletal data. To address these challenges, we propose a multimodal fusion framework that leverages optical flow-based key frame extraction, data augmentation techniques, and an innovative fusion of skeletal and RGB streams using self-attention and skeletal attention modules. The model employs a late fusion strategy to combine skeletal and RGB features, allowing for more effective capture of spatial and temporal dependencies. Extensive experiments on benchmark datasets, including NTU RGB+D, SYSU, and UTD-MHAD, demonstrate that our method outperforms existing models. This work not only enhances action recognition accuracy but also provides a robust foundation for future multimodal integration and real-time applications in diverse fields such as surveillance and healthcare.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.