NeuraLyfe

NeuraLyfe is a sideline decision-support system that helps football medical staff catch brain-impact risk before it becomes irreversible. It was designed and built in four days, March 6 to 9, 2026, for the FigBuild 2026 hackathon, entirely in Figma Make.

It tackles Chronic Traumatic Encephalopathy (CTE): a degenerative condition from repeated head impacts that accumulates invisibly and can't be confirmed until after death.

I ideated the concept and helped the team choose it over a rougher direction around dreams, where the pain point never got sharp. Across those four days, I modeled and 3D-printed Halo, the helmet add-on, designed and built the Impact Replay interface, and co-designed the Roster View.

It won 1st Place out of 690 teams.

Turning raw helmet-sensor signals into fast, confident sideline decisions that catch cumulative brain risk before symptoms appear.

Football players take thousands of hits across a career, and repeated sub-concussive impacts are strongly linked to long-term brain damage. Sideline checks only catch visible symptoms, and by then the damage is done.

Staff need to see cumulative impact as it builds, not react once symptoms show.

Helmet sensors today capture force, not how the brain responded. The signal that matters, cumulative neurological stress, goes unmeasured.

The biomarkers are real: p-Tau 217, NfL, and GFAP are each linked to neurodegeneration in published research. Combining them into a live CTE Progression Index is the speculative step, and we kept that line visible instead of letting the index pass as settled science.

The opportunity: track brain-impact risk as it builds, not just flag individual hits.

The core problem was interpretive, not technical: show too much data and it overwhelms; simplify too much and it loses credibility.

The question I kept returning to: what does a sideline medic need to know in the next five seconds?

Two decisions fixed the shape of the system before any screen existed: where the sensing lives, and who acts on what it finds.

Halo, a helmet add-on, senses what force data can't: EEG for brain connectivity, biomarkers for early neurological stress, and an impact camera that reconstructs hits.

The sideline interface turns those signals into three questions: who needs attention, what's happening in their brain, and what caused it.

Staff can't explore data mid-game, so the interface leads with triage and reveals depth only on demand:

· Roster View: who needs attention, at a glance

· Brain View: where stress is building

· Impact Replay: which play caused it

I pressure-tested one question throughout the Figma Make build: can staff go from alert to decision in seconds?

Halo

An add-on, not a new helmet, so teams keep the equipment they already trust. EEG maps brain connectivity, biomarker sensors catch early damage indicators, and the impact camera ties each force event to a moment on the field.

I modeled the Halo in Rhino, rendered it in KeyShot, and printed it on a Bambu Lab X1C within the four days, so the sensing layer existed as an object on the table and not only as a render on a slide.

Roster View

Ranks players by medical urgency, weighing cumulative impacts, recent hit severity, and brain health, so a static player list becomes a live risk map.

Brain View

Maps neurological stress by region in 3D. A risk score says how much. Brain View shows where, which matters most when repeated impacts hit the same region.

Impact Replay

Traces an alert back to the exact play: when the hit happened, how severe it was, which regions were affected, and whether to pull the player now.

The Protocol

Sensing only matters if it ends in a call someone can defend. Every player sits in one of five states, and the last two are not advisory.

Together: identify risk, inspect impact, act with context.

NeuraLyfe's claim: brain-injury risk shouldn't stay hidden until symptoms appear.

The judges recognised the visuals, the film, the storytelling, and especially the Halo prototype, which could be picked up rather than imagined.

Designing for health means designing for interpretation, not data display. A system can collect perfectly accurate signals, but if staff can't read them under pressure, those signals never become care.

High-Pressure Interfaces Need Different Logic

The first question is never "what does all the data say?" It's "who needs help right now?" That pushed me to cut competing information and make the most urgent cases immediately visible.

What I Would Do Next

Clinical validation with sports-medicine professionals, testing whether the decision flow holds up in real sideline calls.

NeuraLyfe showed me that making hidden risk visible is a design problem. The harder part is making that visibility useful when it matters most.