This thesis investigates the development of an automatic monitoring system for patients in Intermediate Care Units (IMUs) at Karolinska University Hospital in Solna and Huddinge, Stockholm. The project aims to support nurses by reducing the burden of continuous visual supervision and ensuring the detection of patient agitation or risk-related behaviors. Two deep learning architectures were explored and compared: MoViNet-A5, a lightweight 3D convolutional neural network optimized for real-time video recognition, and PredFormer, a transformer-based model designed for capturing long-range spatiotemporal dependencies. The models were fine-tuned on a custom dataset, initially composed of simulation videos collected with a multi-camera Raspberry Pi setup, with the intention of later extending to real patient data under ethical approval.