Ultralytics YOLO26: fall detection and email alert setup

This tutorial uses Ultralytics Platform to train a YOLO26 nano fall detector, then exports the model for local Python inference. The dataset covers standing, sitting and falling. The presenter selects 100 epochs and an RTX Pro 6000, checks training losses and precision metrics, and tests predictions before downloading the PT weights.

The dataset is small, and the presenter notes missed detections in recall despite good precision. The example demonstrates a workflow rather than establishing reliability for safety monitoring. Cloud deployment sends images to a server; the exported model supports local AI processing. The presenter says these small models can run on a CPU and describes export options including ONNX, OpenVINO, TensorRT for Nvidia hardware and Core ML for Apple hardware.

The Python walkthrough uses OpenCV to capture an RTSP stream, webcam or video file. YOLO tracking supplies bounding boxes, confidence scores and person IDs, while the alert logic selects the falling class. A queue and a separate email worker keep sending mail from blocking inference.

Gmail delivery requires sender and recipient addresses plus an app password. Alerts include the person ID, confidence, timestamp and an annotated image. In the demonstration, the presenter receives an email for a detected fall and reports no repeated alerts despite detection flicker. Email delivery still requires a network connection.