Abstract :

Visually Impaired Individuals Face Significant Challenges In Navigating Their Surroundings Safely And Independently. To Address This Issue, This Project Proposes A VoiceBased Assistance System For Blind People That Integrates Machine Learning, Computer Vision, And IoT Technologies To Provide Real-time Environmental Awareness And Emergency Support. For Object Detection, The System Employs MobileNet SSD With The COCO Dataset, Implemented Using OpenCV, To Recognize Common Objects In The Environment. The Identified Objects Are Then Announced To The User Through Voice Feedback Generated Using Pyttsx3 And Pygame, Enabling Real-time Situational Awareness. The System Also Incorporates An IoT Module Using NodeMCU ESP8266 And Blynk IoT Platform To Ensure Safety And Remote Monitoring. An MPU6050 Accelerometer-gyroscope Sensor Is Used For Fall Detection, While A Panic Button Allows The User To Send Immediate Distress Signals. For Location Tracking, A GPS Module Provides Live Coordinates To Caregivers, Enhancing User Safety During Emergencies. Additional Safety Indicators Include A Buzzer And LEDs (red/green) To Signal System Status And Alerts. By Combining AI-driven Object Recognition With IoT-based Monitoring And Emergency Response, This System Not Only Aids Navigation Through Real-time Voice Guidance But Also Enhances Security Through Fall Detection And Live Tracking. The Proposed Solution Is Costeffective, Portable, And Highly Suitable For Realworld Applications, Significantly Improving Mobility And Independence For Visually Impaired Individuals. Keywords— : IoT, ESP8266, Obstacle Detection, MAX30100, ESP32-CAM, Remote Healthcare

Published:

02-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

110-117

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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