Project 02 / Accessibility
Hands-free Mac control through hand and face gestures. Built for accessibility — designed for people with mobility limitations who need a better way to use a computer.
A real-time computer vision system using MediaPipe and OpenCV that translates hand and face gestures into Mac system actions — desktop switching, volume, scrolling, playback, and more.
Runs as a lightweight background process on an M3 MacBook Air. The M3 neural engine handles MediaPipe inference efficiently with no noticeable performance hit.
Wave gesture toggles brainrot mode for hands-free TikTok/YouTube scrolling using head nods. Safety lockouts prevent accidental triggers when you're touching your face.
Full gesture system working. Both hand and face detection running simultaneously. Safety features (hand lockout, face/hand distance check) implemented and tested.
Separate mudra_detect.py module detects 13 Bharatanatyam mudras with confidence scoring and snapshot saving.