Overview

A modern AAC (Augmentative & Alternative Communication) app that helps non-verbal users express themselves faster.
It combines predictive text (TensorFlow.js), accessible UI, and a caregiver analytics dashboard for insight-led support.

Problem

  • AAC tools are often slow and cognitively heavy.
  • Caregivers lack visibility on what’s working (or not) during sessions.
  • Accessibility (WCAG) and privacy (GDPR) are frequently bolt-ons, not fundamentals.

Solution

  • Predictive AI suggests next words/phrases in context; improves with usage.
  • Accessible UI (high-contrast themes, large touch targets, offline first).
  • Caregiver dashboard: session logs, vocab usage, time-to-utter metrics.

Key Features

  • Next-word prediction via TF.js + custom tokenizer.
  • Personal vocab folders with pictograms; grid size & theme preferences.
  • Caregiver dashboard (Firebase + charts): top phrases, session durations.
  • WCAG 2.1 AA: keyboard/switch navigation, ARIA roles, focus states.
  • GDPR-first: anonymized events, explicit consent flows, export/delete data.

Architecture

  • Client: React / React Native (Kiosk mode on tablets)
  • Model: TensorFlow.js in-browser; optional server-assisted re-ranking
  • Storage: Firebase (Auth/Firestore/Storage)
  • Telemetry: Minimal, aggregated events; per-user opt-in

Outcomes

  • 35–50% faster average time-to-utter for common phrases (pilot, n=7).
  • +40% caregiver satisfaction with session clarity (survey).
  • Reduced input errors with large targets & latency-friendly cache.

My Role

  • End-to-end build, model integration, WCAG compliance, privacy workflows.
  • UX research with caregivers; rapid iteration via weekly pilots.

Tech Stack

React, React Native, TensorFlow.js, Firebase (Auth/Firestore/Hosting), Tailwind, Charting

Screens / Media

Notes on Privacy & Safety

  • Anonymization by default, per-user data export, short retention windows.
  • Clear consent and session visibility for caregivers and admins.