Pong C Yuen from Hong Kong Baptist University will present a talk on remote photoplethysmography (rPPG) detection. The talk will review the development of rPPG detection, share recent research, and discuss future directions. rPPG is a technology for non-contact computer vision and healthcare applications like heart rate estimation. Why it matters: Advancements in rPPG could enable new remote patient monitoring and diagnostic tools in the region, reducing the need for physical contact.
Researchers from MBZUAI introduced RP-SAM2, a method to improve surgical instrument segmentation by refining point prompts for more stable results. RP-SAM2 uses a novel shift block and compound loss function to reduce sensitivity to point prompt placement, improving segmentation accuracy in data-constrained settings. Experiments on the Cataract1k and CaDIS datasets show that RP-SAM2 enhances segmentation accuracy and reduces variance compared to SAM2, with code available on GitHub.
Researchers at MBZUAI developed a method to measure vital signs using webcams by analyzing color intensity changes in facial blood flow. They built a digital twin system that uses machine learning to combine heart rate, respiratory rate, and blood oxygen level measures. The system displays real-time vital sign information, enabling remote patient triage. Why it matters: This research contributes to the advancement of telemedicine, potentially improving healthcare access in underserved regions and aligning with UN Sustainable Development Goal #3.
Dezhen Song from Texas A&M University presented a talk on Co-Modality Active sensing and Perception (C-MAP) for robotics, covering sensor fusion for autonomous vehicles, augmented reality, and remote environmental monitoring. The talk highlighted lessons learned in sensor fusion using autonomous motorcycles and NASA Robonaut as examples. Recent works in robotic remote environment monitoring, especially focused on subsurface surface void and pipeline mapping were discussed. Why it matters: This research explores sensor fusion techniques to enhance robot perception, which could improve the robustness and capabilities of autonomous systems developed and deployed in the Middle East, particularly in challenging environments.
This paper introduces a self-supervised learning method for point cloud analysis using an upsampling autoencoder (UAE). The model uses subsampling and an encoder-decoder architecture to reconstruct the original point cloud, learning both semantic and geometric information. Experiments show the UAE outperforms existing methods in shape classification, part segmentation, and point cloud upsampling tasks.
A team led by the Technology Innovation Institute (TII) in Abu Dhabi has developed NATHR-G1, a ground penetrating radar for detecting landmines and unexploded ordnance. The project, involving researchers from Colombia, Germany, Sweden, and Switzerland, builds on earlier work using radar to detect buried objects. NATHR-G1 incorporates machine learning for advanced signal processing and object identification. Why it matters: This humanitarian application of AI and robotics based in the UAE could significantly reduce casualties from landmines and other explosive remnants of war.
Eduardo da Veiga Beltrame, bioinformatics lead at ImYoo (a Caltech spinout), presented on scalable methods for single-cell omics data analysis, including kallisto|bustools and scvi-tools. He highlighted their use in ImYoo's decentralized longitudinal study on Inflammatory Bowel Disease (IBD), where patients self-collect capillary blood samples. Beltrame also discussed his research on STEM education programs in Brazil as a visiting scholar at UC Berkeley. Why it matters: This highlights the growing trend of decentralized clinical studies leveraging advanced single-cell technologies for precision medicine, showcasing the potential of remote data collection and analysis in understanding complex diseases.