Nasogastric tube (NGT) feeding supports over 10 million patients globally, particularly those with stroke-induced dysphagia, Parkinson's disease, and cognitive impairments. However, current practices face three fundamental limitations. First, caregivers often lack knowledge of the nutritional content of homemade blended meals, leading to high malnutrition rates among long-term NGT patients. Second, commercial solutions struggle to analyze blended meals in large containers due to light path distortion. Third, gastric residual volume (GRV) assessments rely on subjective nurse evaluations, resulting in incomplete records and increased feeding intolerance. To address these challenges, this demo introduces NutriBump, an innovative system designed to enhance NGT feeding through closed-loop control. Utilizing advanced spectral analysis technology, the system accurately assesses food nutrients and employs multi-modal sensors for automatic gastric fluid aspiration and analysis. By integrating long-term nutritional intake, digestion data, and health status, NutriBump leverages large language models and AI agents to generate personalized nutrition reports automatically. This closed-loop nutrition management solution improves the accuracy of blended meal analysis and reduces reliance on subjective digestion assessments.

Hypertension affects 1.28 billion adults, but blood pressure alone is insufficient for accurate hypertension detection. In this paper, we identified a range of multi-dimensional hemodynamics as novel biomarkers that can offer more comprehensive cardiovascular insights for detecting hypertension. We design a novel system that collects ballistocardiogram (BCG) signals from an optic fiber sensor mat. It features a multi-task, multi-branch, unsupervised domain adaptation learning framework, enabling simultaneous prediction of five hemodynamic biomarkers for hypertension detection. In a 4-month trial with 85 subjects, Hyde achieved 97.65% average accuracy for hypertension detection.