08 · CV · 2026
BlinkQuest
Privacy-first eye wellness & blink rate tracking via on-device computer vision.
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By Amit Chakraborty — Senior Software Engineer, Kolkata, India · Remote worldwide
At a glance
- Role — Founder · Solo Build
- Year — 2026
- Category — CV
- Team — Founder · Solo Build
- Duration — 8 weeks
- Hard part — On-device Computer Vision + MediaPipe + PERCLOS fatigue telemetry
The problem
Prolonged screen usage induces severe blink rate drop and dry-eye syndrome. Existing solutions require streaming invasive webcam feeds to cloud APIs or bulky headsets.
What I did about it
Built a 100% on-device vision pipeline using MediaPipe and TensorFlow Lite running in lightweight frame processors. Video frames are analyzed in transient memory buffers and discarded immediately.
The outcome
Real-time involuntary blink detection, PERCLOS fatigue index scoring, and interactive mini-game breaks with zero bytes of video ever leaving the device.
Clinical eye wellness meets sovereign consumer privacy — zero cloud video processing, zero camera permissions abused.
In detail
BlinkQuest addresses digital eye strain by measuring involuntary blink frequencies and screen gaze fatigue in real time.
Powered by on-device computer vision running MediaPipe face mesh and eye iris landmark detection, it computes PERCLOS (Percentage of Eye Closure) fatigue indices on the edge.
Includes interactive micro-rest exercises and gamified breaks that guide users to natural eye relaxation cycles without disrupting workflows.
Built with
Expo 55 · React Native · MediaPipe · TensorFlow Lite · Next.js 16
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