智能轮椅融合手势控制与多传感器健康监测，提升行动不便者自主安全
An AI-IoT Based Smart Wheelchair with Gesture-Controlled Mobility, Deep Learning-Based Obstacle Detection, Multi-Sensor Health Monitoring, and Emergency Alert System
- 用手套手势实现无手操作导航，成功率95.5%
- YOLOv8检测障碍物达91.5%精度，超声波避障准确率94%
- 实时监测心率等生命体征，异常自动发邮件告警
随着残障及老年人群增加，亟需兼具安全导航与健康监测的智能轮椅。传统轮椅功能单一，多数智能产品成本高、仅支持单一模态且缺乏健康集成。为此，我们提出基于AI-IoT的多功能智能轮椅系统：采用手套式手势控制实现无手操作导航，成功率95.5%；利用YOLOv8进行实时物体检测，配合听觉反馈避障，精度91.5%、召回率90.2%、F1分数90.8%；同时通过超声波实现即时碰撞预警，准确率达94%。心率、血氧、心电图、体温等生命体征持续监测，数据上传ThingSpeak平台，遇异常状态自动触发邮件告警。系统采用模块化低成本架构，集多模态感知于一体，显著提升用户自主性、安全性与独立性，推动科研成果向实际应用落地。
原文摘要 · Abstract (English)
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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