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.
Abstract (translated)
越来越多的残障人士和老年人需要一种既经济又智能的轮椅，这种轮椅能够结合安全导航与健康监测。传统轮椅缺乏动态特性，而许多智能化替代品依然昂贵，并且大多局限于单一功能并且在健康整合方面有限。 鉴于对先进的、个性化的以及可负担得起的辅助技术的迫切需求，我们提出了一种基于AI-IoT（人工智能和物联网）的综合智能轮椅系统。该系统包括用于手控自由导航的手套控制装置，使用YOLOv8进行实时物体检测，并结合听觉反馈来避开障碍物；同时采用超声波传感器以实现即时碰撞避免功能。此外，该系统还能够连续监测心率、血氧饱和度（SpO2）、心电图（ECG）和体温等生命体征信息，并将这些数据上传至ThingSpeak平台，在出现紧急情况时触发电子邮件警报。 我们的设计基于模块化且成本低廉的架构：手部控制装置达到了95.5%的成功率，超声波障碍物检测准确率达到94%，而基于YOLOv8的目标检测则分别实现了91.5% 的精确度、90.2% 的召回率和90.8% 的F1得分。 这种集成的多模式方法提供了一种实用且可扩展的解决方案，以合理的价格显著提升了用户的自主性、安全性和独立性，从而在创新研究与实际应用之间架起了桥梁。
URL
https://arxiv.org/abs/2601.11983
