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

• URL: http://arxiv.org/abs/2601.11983v1
• Date: Sat, 17 Jan 2026 09:37:11 GMT
• Title: An AI-IoT Based Smart Wheelchair with Gesture-Controlled Mobility, Deep Learning-Based Obstacle Detection, Multi-Sensor Health Monitoring, and Emergency Alert System
• Authors: Md. Asiful Islam, Abdul Hasib, Tousif Mahmud Emon, Khandaker Tabin Hasan, A. S. M. Ahsanul Sarkar Akib,
• Abstract summary: 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.<n>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.
• Score: 0.6465251961564605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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