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Explore AI Student Injury Patrol Robot | IoT + Robotics + YOLOv5 | UiTM - ITT569 with 924 views, 320 likes, and 14 comments. Experience the impact of this video content that has captured audience attention.

In this video, we present a student injury patrol robot developed as part of our Intelligent System Engineering project at UiTM Shah Alam. The robot patrols school outdoor areas automatically and uses artificial intelligence to detect fallen or injured students in real time. When an injury is detected, the system immediately captures the GPS location of the incident and sends an alert directly to the school nurse via Telegram with a Google Maps link for a fast response. 🤖 How It Works The robot streams live video using an ESP32-CAM over WiFi to a laptop running a YOLOv5 object detection model. The AI analyses every frame, identifying fallen students based on body position. Once confirmed, the ESP32 DevKit captures the GPS coordinates via a NEO-6M module and triggers the buzzer alarm while the alert is sent wirelessly to the nurse's phone. ⚙️ Components Used ESP32-CAM — live video streaming ESP32 DevKit V1 — GPS tracking + buzzer alert WeMOS D1 R2 — motor control + autonomous patrol NEO-6M GPS module — real-time location L298N motor driver — 4WD movement YOLOv5 — AI fall detection (Python, runs on laptop) Telegram Bot API — instant nurse notification ThingsBoard — cloud dashboard 💰 Total Build Cost: Under RM 200 All components sourced locally in Malaysia from Cytron and Shopee. 📚 Project Details Course: Intelligent System Engineering — UiTM Shah Alam Semester: Mac – July 2026 Technology: IoT, Computer Vision, Robotics, Dashboard 🛠️ Tools & Technologies Arduino IDE — ESP32-CAM + DevKit firmware Python 3 — YOLOv5 fall detection PyTorch + OpenCV — AI model and video processing ThingsBoard — IoT cloud dashboard Telegram Bot API — real-time nurse alert By MUHAMMAD HAFIZ DANIAL BIN ZAIDI (2025381051) MIQHAEL NAQEEB BIN LEMAN (2025160529) MUHAMMAD HAFIZ BIN SAZALI (2025100013) ITT569 CDCS2595B

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