This research develops antibacterial nanostructured surfaces inspired by natural materials such as cicada wings. The engineered surfaces physically rupture bacteria using nanoscale needle-like structures, avoiding traditional antibiotics and reducing the likelihood of antibiotic resistance. The technology could improve infection control in medical devices, implants, and hospital environments.

This research develops a noninvasive method for continuously measuring blood pressure using arterial resonance. Inspired by the physics of vibrating guitar strings, the device gently stimulates arteries and measures their resonance frequencies with ultrasound. The resulting continuous blood pressure waveforms could improve diagnosis of cardiovascular disease without invasive catheterization procedures.

This research uses artificial intelligence to predict the progression of Alzheimer’s disease and cancer using medical imaging data. By analyzing brain scans, tumor scans, and treatment responses, AI models can forecast disease development and treatment outcomes, enabling earlier intervention, more personalized care, and improved quality of life for aging populations.

This research introduces iCares, a smart wound-monitoring bandage designed to detect infection and inflammation before visible symptoms appear. Using biosensors, fluid sampling, and machine learning, the system provides real-time wound analysis, enabling earlier intervention, personalized treatment, reduced complications, and improved healing outcomes for patients with chronic wounds.

This research develops a fast, space-efficient method for assessing fall risk in older adults using wearable sensors and AI analysis of a seated foot-tapping task. Early findings show that slower, inconsistent tapping predicts higher fall risk. The approach could improve prevention strategies, reduce healthcare costs, and help older adults maintain independence.

This research addresses excessive false alarms in hospital medical devices, which burden staff and distress patients. By detecting and filtering noisy data, the proposed system prevents false alerts while preserving true ones. Early results show complete removal of false alarms, improving efficiency, patient experience, and clinical response in healthcare settings.

This research uses wearable data and AI to detect disease earlier by analyzing continuous health signals rather than isolated clinical snapshots. By personalizing models to individual baselines, the system identifies subtle changes linked to conditions like infections, heart issues, and mental health crises, enabling earlier intervention and potentially saving lives.