In this project, we further developed our robust leader-follower protocol which autonomously coordinates a group, or swarm, of devices. The system is designed to seamlessly adapt to devices dropping out of the swarm unexpectedly and to any new devices joining the network. We focused on two main tasks this year: formal verification of the protocol and developing a demonstration with mobile robots. The formal verification proved that our protocol satisfies both safety and adaptability requirements.

At Design Day, we will have an interactive simulation, which shows how our protocol can coordinate up to 50 robots. We will also present a video of our robot demonstration, which uses five TurboPi robots as devices which autonomously work to each perform a task within different quadrants of a large grid.

Peri-implant mucositis is an inflammatory gum condition of tissue surrounding a dental implant, causing redness, swelling, and bleeding upon probing. If left untreated, peri-implantitis may develop, causing a breakdown of periodontal fibers, bone, and gums, necessitating implant removal. Implant providers need a method to detect peri-implant mucositis to reduce the incidence of peri-implantitis to less than 5% post-implantation. Current methods cannot determine, much less predict, early inflammations. Our method measures the pH of fluids around implants to detect inflammations early with a high sensitivity.

Neurological disorders impact a large percentage of the global population and are a vast area of research in the clinical field. However, state of the art diagnostic measures such as MRI and CT scans are invasive and expensive. Saccades, rapid fixations in eye movements, are a promising but underutilized non-invasive biomarker for neurological abnormalities due to limited publicly available data and privacy concerns. To address this, we developed a pose-guided video generation model that produces synthetic saccades of three types: normal, bilateral hypermetria, and bilateral hypometria that mimic real eye movement patterns observed in clinical settings. We trained an MViT-V2 video classification model on the synthetic data as a baseline and tested its performance on clinical saccade data. Our approach demonstrates the potential of synthetic data to enable accurate and scalable saccade-based diagnostics, reducing the dependency on invasive imaging.

This project introduces the first all-in-one scar protector patch that combines microneedling, hydrocolloid healing, vitamin C delivery, and UV protection into a single, easy-to-use solution. Designed for individuals affected by long-term scarring from surgery, injury, or acne, the patch supports skin regeneration, reduces discoloration, and shields healing skin from sun damage. By integrating clinically supported therapies into a seamless patch format, our scar protector improves both aesthetic and medical outcomes, restoring skin health and boosting confidence.