Assistive Biometrics Lab

Local-first test queue for the camera collector: richer emotions, AAC-style wants, hands/body/audio, and AR mask experiments. No raw biometric storage by default.

AAC + child UI vocabulary

The prototype now separates pure emotion hints from practical communication cards. Wants include daily needs plus core words that are useful across many situations.

Pure emotions

Happy, calm, sad, angry, scared, surprised, confused, tired, hurt, overloaded, bored, sound.

🙂 happy😌 calm😢 sad😠 angry 😨 scared😳 wow🤔 huh🥱 tired 🤕 ouch🫨 too much😐 bored🗣️ sound

Wants + core words

Drink, eat, toilet, outside, play, sleep, help, break, quiet, hug, more, stop, yes, no, music.

🥤 drink🍽️ eat🚽 toilet🌳 outside 🧸 play😴 sleep🆘 help🛑 break 🤫 quiet🤗 hug➕ more✋ stop ✅ yes❌ no🎵 music

Open-source / free candidates to test

MediaPipe Face Landmarker

Current base layer for face mesh, blendshapes, head pose, eye openness, and mouth movement.

Official web docs

MediaPipe Hands + Pose

Current base layer for fingertips, pinch/open hand, body keypoints, leaving-frame, and restlessness.

Hand docs

TensorFlow.js face landmarks

Alternative browser model to compare face stability, long-distance detection, and runtime cost.

GitHub

Jeeliz FaceFilter

AR-mask candidate for face-attached effects if the current canvas mask needs stronger tracking.

GitHub

MindAR face tracking

Another browser AR candidate for face filters and anchored overlays.

GitHub

AAC references

Vocabulary direction: wants, feelings, routine needs, and reusable core words for communication.

ASHA AAC overview

Wearables

Future optional layer: Apple Watch or smart band heart rate, HRV, movement, activity, and rest context.

Apple health and fitness docs

Next test queue

Mouth + tongue

Test mouth crop classifier or segmentation; current tongue value is only a mouth-open heuristic.

Gesture layer

Detect hand-to-mouth, pointing, self-touch, stop gesture, reach, wave, and repeated movement bursts.

Expression classifier

Train per-child classifier on blendshapes, pose, hands, audio, time context, and caregiver labels.

Audio layer

Classify nonverbal vocalization intensity, pitch contour, repeated hums, and distress bursts.

Wearable physiology

Add consented Apple Watch / smart band signals as context, not diagnosis: heart rate, HRV, motion, sleep/rest.