An Android application designed as an all-in-one assistant for blind and visually impaired users — combining voice-driven interaction, accelerometer-based footstep counting for indoor navigation, GPS + Google Geofence for outdoor navigation and unsafe-area alerts, and a jolt module that detects unusual shaking and auto-sends a live location SMS to trusted contacts. Tested on 30 users with a System Usability Scale score of 72.27.
Around 285 million people live with visual impairment worldwide, and everyday tasks — orienting in a room, crossing a street, calling for help — can be cumbersome or unsafe. Smartphones already carry the sensors needed to help, but most existing apps focus on a single problem (turn-by-turn navigation, screen magnification, barcode reading, etc.) and assume the user can comfortably operate a touchscreen.
This work builds a single Android application that addresses navigation, safety, communication, and daily utilities together, with an interaction model designed for non-visual use: voice command first, with touch, hotkey buttons, and shake as alternatives, all paired with audio feedback.
Two cooperating sections share a central "Blind Channel" controller. The Blind Assistant Section (BAS) is configured by a trusted helper — emergency contacts, named locations, footstep counts to common destinations, and unsafe geofenced areas. The Blind People Section (BPS) is used by the VI person — voice recognition, touch, jolt-detection, daily-info, and unsafe-area indication modules.
The pedometer is built on the accelerometer (most phones lack a dedicated pedometer chip), and the jolt module turns unusual phone shaking into a one-shot emergency: it auto-calls a trusted number and SMSes a live Google Maps location link to all stored contacts.
The system is module-based around a central Blind Channel. The assistant-facing side (BAS) sets up emergency contacts, named footstep destinations, and unsafe geofenced areas; the user-facing side (BPS) handles voice, touch, jolt, daily info, and live unsafe-area alerts. Indoor navigation runs on the accelerometer pedometer; outdoor navigation uses GPS and Google Maps with online and offline modes.
"Go to washroom", the app guides them by counting steps via the accelerometer — no dedicated pedometer hardware required.>3 rapid shakes (fall or distress) and auto-calls a trusted contact while sending a live Google Maps location SMS to all stored contacts. The Indication module uses Google Geofence to warn the user whenever they enter an area marked unsafe.The two charts below reproduce the paper's performance experiments with Plotly.js. The first compares voice-recognition response time for ten male and ten female test cases — toggle between Best, Average, and Worst response — and shows the gender-independent ≈ 6.3 ms average. The second plots location-send time vs. distance across ten test cases ranging from 1.5 km to 244 km, demonstrating that delivery time stays near 7.6 ms regardless of geographic distance.
ⓘ All values are from the paper. Voice data — Table 2 (10 male + 10 female test cases, each averaged over 30 repetitions). Location data — Table 3 (10 sender/receiver pairs across distances from 1.5 km to 244.2 km, response time in milliseconds).
| Platform | Android · Android Studio |
| Sensors | Accelerometer (steps + jolt) · GPS · Microphone · Camera |
| Maps / Geo | Google Maps API · Google Geofence |
| Interaction | Voice command (dictionary) · touch + audio feedback · hotkey buttons · shake |
| Modules | BAS: Contact · Permission · Pedometer · Area | BPS: VoiceRec · Touch · Jolt · Required · Indication |
| Testing | Voice 10×30 reps (male + female) · Location 10 distances (1.5–244 km) · SUS 30 users |
| Published | Springer · ICONCS 2020 · LNICST vol. 325 · pp. 581–592 |
| DOI | 10.1007/978-3-030-52856-0_46 |
Springer chapter (ICONCS 2020 · LNICST 325 · pp. 581–592) · source on GitHub.