An AI-IoT Based Smart Wheelchair with Gesture-Controlled Mobility, Deep Learning-Based Obstacle Detection, Multi-Sensor Health Monitoring, and Emergency Alert System

TL;DR

This paper presents an affordable, AI-IoT based smart wheelchair integrating gesture control, deep learning obstacle detection, multi-sensor health monitoring, and emergency alerts to enhance safety, autonomy, and health management for users.

Contribution

It introduces a comprehensive, low-cost, modular smart wheelchair system combining gesture control, deep learning obstacle detection, and health monitoring, advancing assistive technology integration.

Findings

Gesture control success rate of 95.5%

Obstacle detection accuracy of 94%

Object detection F1-score of 90.8%

Abstract

The growing number of differently-abled and elderly individuals demands affordable, intelligent wheelchairs that combine safe navigation with health monitoring. Traditional wheelchairs lack dynamic features, and many smart alternatives remain costly, single-modality, and limited in health integration. Motivated by the pressing demand for advanced, personalized, and affordable assistive technologies, we propose a comprehensive AI-IoT based smart wheelchair system that incorporates glove-based gesture control for hands-free navigation, real-time object detection using YOLOv8 with auditory feedback for obstacle avoidance, and ultrasonic for immediate collision avoidance. Vital signs (heart rate, SpO$_2$, ECG, temperature) are continuously monitored, uploaded to ThingSpeak, and trigger email alerts for critical conditions. Built on a modular and low-cost architecture, the gesture control achieved a 95.5\% success rate, ultrasonic obstacle detection reached 94\% accuracy, and YOLOv8-based object detection delivered 91.5\% Precision, 90.2\% Recall, and a 90.8\% F1-score. This integrated, multi-modal approach offers a practical, scalable, and affordable solution, significantly enhancing user autonomy, safety, and independence by bridging the gap between innovative research and real-world deployment.