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 work tackles the need for affordable, autonomous wheelchairs by presenting an AI-IoT system that integrates glove-based gesture control, YOLOv8-based real-time obstacle detection, ultrasonic collision avoidance, and continuous multi-sensor health monitoring with cloud-based alerts. It introduces a modular, low-cost architecture built around ESP32s, a Raspberry Pi, and ThingSpeak to enable local operation and remote monitoring. Key results show a gesture-control success rate of $95.5\%$, ultrasonic obstacle detection at $94\%$, and YOLOv8 object-detection metrics of $Precision = 91.5\%$, $Recall = 90.2\%$, and $F1 = 90.8\%$, achieved on a compact dataset and edge devices. The proposed solution substantially enhances user autonomy, safety, and accessibility, providing a practical roadmap toward real-world deployment and broader adoption in assistive mobility contexts.

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.

Figures (11)

• Figure 1: System Architecture of IoT Based Smart Wheelchair
• Figure 2: Working Procedure of Wheelchair Base
• Figure 3: Health Monitoring Procedure
• Figure 4: Object Detection Procedure
• Figure 5: YOLOv8 Architecture Overview