GrandGaze

Grand Gaze

Inspiration

Many elderly residents and individuals with severe mobility limitations struggle to communicate their needs in care environments. Even when assistive tools exist, they often focus only on sending requests, not on understanding the patterns behind those interactions. We were inspired to explore how gaze — one of the most natural and reliable human signals — could be used not only for communication but also for understanding comfort, effort, and unmet needs. GrandGaze aims to transform eye movements into meaningful insights that help caregivers respond faster and more effectively.

What it does

GrandGaze is a gaze-powered communication and care-insight system designed for residents in assisted care environments. Residents interact with a gaze-controlled interface to select needs such as water, repositioning, or assistance. On the caregiver side, the system analyzes gaze interaction patterns to surface insights about communication effort, potential discomfort, and recurring support needs. Caregivers can review resident insights, document care actions, and track care history through a mobile interface. The platform also includes safeguards such as human-in-the-loop decision making, role-based access controls, and an audit log to ensure transparency and accountability.

How we built it

We designed GrandGaze as a two-sided system consisting of a resident interface and a caregiver dashboard. The resident interface simulates gaze-based communication where users select requests using dwell-based eye interaction. The caregiver side translates these interactions into insights about effort, patterns, and likely needs. The prototype was built using Figma for interface design and interactive prototyping, allowing us to simulate the full workflow from resident request to caregiver action. We designed the system using a structured design system, and iOS-inspired UI patterns o maintain visual clarity and consistency.

Challenges we ran into

One challenge was designing a system that interprets gaze behavior without over-automating care decisions. We had to carefully design interactions where AI suggestions assist caregivers rather than replacing their judgment. Another challenge was handling edge cases such as false gaze signals, privacy concerns, and caregiver accountability. This required designing features like editable care notes, role-based access controls, and a full audit log to ensure the system remains trustworthy. Balancing clarity and information density in the caregiver dashboard was also difficult. We wanted to surface meaningful insights without overwhelming caregivers with data.

Accomplishments that we're proud of

We are proud of creating a prototype that demonstrates a complete caregiving workflow, not just isolated screens. The system shows how a resident request can translate into actionable insights and documented care actions. We are also proud of designing a product that considers responsible AI use in healthcare, with safeguards such as human verification of care actions and transparent activity tracking. Finally, we are proud of designing both sides of the experience ( resident interaction and caregiver decision support) that work together to create a more responsive care environment.

What we learned

Working on GrandGaze helped us better understand how assistive technology must balance usability, trust, and real-world care workflows. We learned that gaze interaction can provide meaningful signals about user effort and attention, but interpreting these signals responsibly requires careful design and human oversight. We also learned how important it is to design systems that support care teams, not just individual users, by including documentation tools, transparency mechanisms, and clear communication flows.

What's next for GrandGaze

The next step is to move beyond a prototype and explore real-time gaze tracking and signal analysis using eye-tracking hardware and computer vision models. We would also expand the system to support long-term pattern detection, helping caregivers identify trends such as fatigue, discomfort, or changes in cognitive effort over time. Finally, we want to test GrandGaze in real care environments to evaluate how gaze-driven insights can improve response time, caregiver awareness, and resident quality of life.