AccessBot

AccessBot is an AI-driven assistive technology solution designed to empower blind and deaf individuals with safe, independent mobility.

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Inspiration

AccessBot was inspired by the visible gap between technological advancement and real-world accessibility. While navigation systems optimize for time and distance, they rarely account for accessibility constraints such as ramps, elevators, or obstacle-heavy environments. We observed that people with visual, hearing, or mobility impairments often rely on assistance for tasks that should be independently manageable. The goal was to design a system where accessibility is treated as a core routing constraint, not a secondary feature. We wanted to build a scalable, AI-driven platform that improves independent mobility using practical hardware and optimized algorithms.

What it does

AccessBot is an AI-powered accessibility navigation system that combines smart glasses, accessible route optimization, and crowdsourced mapping.

The system:

  • Detects real-time obstacles using onboard computer vision
  • Provides haptic and audio feedback for safe navigation
  • Computes accessibility-aware routes using weighted path optimization
  • Integrates AR overlays for guided movement
  • Allows users to map and validate accessibility features

Traditional routing minimizes:

$$ \min \sum_{i=1}^{n} w_i $$

AccessBot modifies this to:

$$ Cost = \alpha \cdot d + \beta \cdot a $$

Where:

  • ( d ) = distance
  • ( a ) = accessibility penalty
  • ( \alpha, \beta ) = priority weights

This ensures routes are both efficient and usable.

How we built it

Hardware

We developed a wearable prototype using:

  • ESP32-S3 / ESP32-CAM
  • INMP441 microphone
  • MAX98357A amplifier
  • Vibration motor module
  • OLED display
  • Bone-conduction speaker

The device performs lightweight edge inference and syncs with a mobile application.

Software

  • Optimized computer vision pipeline for obstacle detection
  • Distance estimation using projection geometry:

$$ d = \frac{f \cdot H}{h} $$

  • Accessibility-aware routing algorithm
  • Crowdsourced accessibility database with validation scoring
  • Mobile interface for navigation and AR overlays

The architecture separates edge inference (micro-level awareness) from mobile-based route planning (macro-level navigation).

Challenges we ran into

  1. Hardware Limitations
    Running real-time inference on ESP32-class hardware required aggressive model optimization and memory management.

  2. Latency Constraints
    Navigation feedback must satisfy:

$$ Latency \leq 100 \text{ ms} $$

Maintaining this under variable lighting and movement conditions was challenging.

  1. Environmental Variability
    Detection accuracy fluctuated in low-light or cluttered environments.

  2. Data Reliability
    Accessibility data is often incomplete. We implemented a confidence scoring model:

$$ C = \frac{\sum r_i}{n} $$

to improve trustworthiness.

  1. Multimodal UX Design
    Designing a unified system for blind, deaf, and wheelchair users required careful abstraction of feedback mechanisms.

Accomplishments that we're proud of

  • Successfully built a working smart glasses prototype.
  • Implemented accessibility-aware routing instead of simple shortest-path logic.
  • Achieved low-latency obstacle detection on embedded hardware.
  • Designed a scalable crowdsourced accessibility validation system.
  • Integrated hardware and mobile software into a synchronized ecosystem.

What we learned

  • Accessibility is fundamentally a systems engineering problem.
  • Edge AI requires balancing model accuracy with computational constraints.
  • In assistive technology, reliability and predictability are more important than feature complexity.
  • Real-world deployment demands robust fallback mechanisms.
  • User-centered thinking significantly improves system design decisions.

What's next for AccessBot

  • Replace geometric distance approximation with depth estimation models optimized for edge deployment.
  • Integrate SLAM for improved indoor navigation accuracy.
  • Expand the accessibility dataset through structured partnerships.
  • Improve power efficiency and wearable ergonomics.
  • Deploy pilot testing with real users to collect feedback and iterate.

Our long-term goal is to evolve AccessBot into a standardized accessibility intelligence layer that can integrate with existing navigation ecosystems.

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