IoT-powered wearable assist device for visually impaired

Lokesh S ¹, Sukesh P ¹, Tejaswini S ¹

⁰Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamilnadu, India.

Disability and Rehabilitation. Assistive Technology +

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November 3, 2025

View abstract on PubMed

Summary

This study introduces an Internet of Things (IoT)-enabled wearable device to enhance mobility and environmental awareness for visually impaired individuals. The assistive technology uses advanced AI for real-time obstacle detection and navigation, improving independence.

Area Of Science

Assistive Technology
Computer Vision
Internet of Things (IoT)

Background

Visual impairments significantly impact mobility and environmental interaction.
Existing assistive devices often lack real-time environmental perception and adaptive navigation capabilities.
There is a need for intelligent, wearable solutions to enhance independence for the visually impaired.

Purpose Of The Study

To develop and evaluate an Internet of Things (IoT)-enabled wearable assistive device for improving obstacle perception, mobility, and environmental interaction for visually impaired individuals.
To create a unified framework integrating real-time object detection, facial recognition, and obstacle sensing for autonomous navigation.
To assess the device's effectiveness in facilitating secure and independent navigation.

Main Methods

A prototype was developed using an ESP32-CAM module, Arduino microcontroller, ultrasonic sensor, and text-to-speech feedback.
Employed deep learning (ResNet-50 CNN, YOLOv4) and machine learning (SVM) algorithms for facial and object detection.
Evaluated system performance using accuracy, precision, recall, and F1-score under diverse environmental conditions.

Main Results

The system achieved over 90% recognition accuracy with minimal computational latency, suitable for real-time use.
The hybrid SVM-YOLOv4 model demonstrated robust multi-object recognition across various lighting and environmental conditions.
Quantitative results validated the wearable device's effectiveness and adaptability for assistive purposes.

Conclusions

The IoT-enabled wearable device shows significant potential to improve mobility, awareness, and independence for the visually impaired.
Integration of computer vision and IoT enhances practical rehabilitative applications.
Future work includes user validation to optimize usability and comfort, with potential for broader assistive technology applications.

Keywords:

Arduino IoT (internet of things) computer vision face recognition machine learning object detection visually impaired wearable device