BrightAR - Alcohol awareness platform

Project description

BrightAR transforms the traditional Alcoholbrillen (alcohol awareness goggles) into an immersive Mixed Reality experience for the Meta Quest 3. The central design challenge is: how can we simulate the perceptual and motor effects of alcohol intoxication in real-time passthrough AR, while making the experience measurably impactful for behaviour change? Goal of the project is to shift the emotional journey from the social "fun" around alcohol toward genuine awareness and lasting impact.

Context

BrightAR operates at the intersection of public health, education, and immersive technology. The domain is alcohol-prevention and behaviour change, specifically targeting the gap between awareness and actual perceptual understanding of intoxication's effects.

In the Netherlands, alcohol misuse among young people remains a persistent public health concern, with organisations such as Trimbos Instituut and addiction-care providers (verslavingszorg) actively seeking more effective prevention tools. Traditional approaches such as lectures, physical "drunk goggles," and informational campaigns struggle to create lasting behaviour change because they fail to provide a visceral, personal understanding of impairment, and critically, none can measure their own effectiveness.

The project responds to a clear commercial and societal gap: no existing prevention programme can prove its impact with data. By digitising the physical alcohol goggle into a Mixed Reality platform, BrightAR sits within the growing field of VR/AR for health education and training simulation, alongside applications in driving instruction and corporate safety training.

The primary target groups are schools, driving schools, addiction-care organisations, and companies running internal awareness programmes. Key domain stakeholders include Trimbos Instituut and NovaDick, who act as gatekeepers for credibility and adoption within Dutch healthcare and education systems.

Results

Outcomes

The project's primary outcomes fall into three categories: a working technical prototype, an interaction model validated through hands-on testing, and early insights into the design challenges of simulating intoxication within Mixed Reality.

Product 1 — Mixed Reality Simulation Prototype
A functioning Unity-based application running on Meta Quest 3 demonstrates real-time, passthrough-based simulation of alcohol impairment. The prototype combines several layered effects — peripheral vision loss (vignette), reduced contrast and desaturation applied directly to the passthrough camera feed, animated spatial distortion (Perlin noise-driven warping), and balance/coordination impairment (camera-space shake) — all driven by a single configurable "alcohol level" parameter. This validates that convincing intoxication effects can be achieved on standalone MR hardware without requiring low-level camera access, working around current passthrough API limitations on consumer headsets.

Product 2 — Real-Time External Control System
A local-network server with a browser-based control interface allows a facilitator to remotely and continuously adjust the alcohol level experienced by the user in real time, via a simple slider. This validates the project's core interaction model: a third party (teacher, trainer, or supervisor) can dynamically calibrate the intensity of the experience during a live session, independent of the headset wearer, supporting supervised group settings such as classrooms.

Product 3 — Gamified Consumption Mechanic
A custom 3D beer cup model was integrated as an interactive in-experience object. Each simulated beer consumed increments a counter and proportionally increases the intoxication effect intensity, up to a defined maximum. This validates an alternative, more embodied interaction model to the external slider — letting the user's own in-experience choices, rather than only external control, drive escalation of impairment. It also creates a natural progression curve that mirrors real-world drinking sessions, which is valuable for the eventual behaviour-change and reflection components of the platform.

Validation & TRL Positioning
The current outcomes sit at approximately TRL 3–4 (Technology Validated in Lab): individual components (passthrough manipulation, distortion shaders, remote control, gamified interaction) have each been built and function correctly in isolation and in early combined testing on the target hardware. Validation so far has been internal (within the project team) and functional rather than with end users, focused on confirming technical feasibility rather than measuring behaviour-change impact.

This represents meaningful progress from TRL 1–2 (concept only) toward TRL 4–5, since the core technical risks identified at project start — passthrough compositing limitations, real-time external control feasibility, and effect believability — have now been substantially de-risked through working implementations. The next validation step required to progress toward TRL 5–6 is structured testing with representative end users (e.g. students or trainees) to assess whether the simulated impairment is perceived as realistic and whether the dual control mechanisms (external and consumption-based) meaningfully support the intended awareness and reflection goals.

Repository