Description :

Monitoring patients in inpatient settings requires a rapid, consistent, and continuous response. The limitations of manual monitoring encourage the use of cameras and deep learning to recognize signs of falls or seizures and then forward alerts to responsible parties. This book discusses an early warning system that connects visual detection, duration validation, event storage, and notification services.

The main advantage of this book lies in its interdisciplinary approach. Readers are introduced not only to the concepts of artificial intelligence, convolutional neural networks, and YOLO26, but also to requirements formulation, client-server architecture design, dataset management, model testing, real-time system evaluation, data security, and user response mechanisms. Thus, model output is understood as part of an information system that must be reliable, measurable, and auditable.

This book is intended for students, lecturers, researchers, software developers, healthcare professionals, and facility managers who want to understand the application of deep learning to emergency monitoring. Its application still requires human verification procedures, patient data protection, the establishment of appropriate operational thresholds, and periodic evaluation in a real-world environment. We hope that this book will be a useful reference and encourage the development of responsible health technology that is oriented towards patient safety.