Integration of YOLOv10 with Edge Computing Design and Application Research of a Non Intrusive Fatigue Monitoring System

Nowadays, with the increasing amount of time students spend studying and staring at the electronic screen, the phenomenon of low concentration, accumulated fatigue, and even damage to physical health and mental health has become very common. Compared with traditional learning aids, which are mostly based on manual check in or wearable devices, they have many obvious disadvantages, such as subjective data collection and large interference. This paper introduces EduHoot , a non invasive learning fatigue monitoring system developed using YOLOv10 and the LicheePi edge computing platform. The system uses visual recognition technology to identify students facial behavior patterns in real time and then realize the local non intrusive fatigue detection and personalized feedback. Finally, the paper establishes an intelligent protection system for “learning health” and puts forward the system architecture, core technology, design concept and development roadmap. Finally, the paper explores the application prospects and social value of smart education based on edge computing through educational technology and AI development trends. Qiyi Qiu | Longyan Qi | Yueting Han | Hengyang Fan "Integration of YOLOv10 with Edge Computing: Design and Application Research of a Non-Intrusive Fatigue Monitoring System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-5 , October 2025, URL: https://www.ijtsrd.com/papers/ijtsrd97548.pdf Paper URL: https://www.ijtsrd.com/computer-science/embedded-system/97548/integration-of-yolov10-with-edge-computing-design-and-application-research-of-a-nonintrusive-fatigue-monitoring-system/qiyi-qiu