Autonomous robotics computer-vision based framework for intelligent remote patient monitoring in health care

We present a novel integrated system for 24/7 patient surveillance that combines (i) an autonomous mobile robot, (ii) static and implantable body sensors, and (iii) advanced AI-based image processing to monitor both the patient’s environment and behavior while minimizing sensor energy consumption. Static bedside nodes and implantable physiological sensors collect vital signs and ambient parameters; missing or corrupted readings are reconstructed in real time by PLSTM and Transformer models, achieving R² scores up to 0.999 and MAE < 0.02. A computer-vision suite (YOLOv11, ResNet, RF-DETR) performs real-time analysis of patient posture and facial expressions, reaching 85% posture-classification accuracy and 87% facial-expression accuracy, with wound-segmentation mAP@0.5 of 92%. The robot navigates semi-structured clinical environments using mission-driven scheduling and learns optimal paths via industrial-learning algorithms; its manipulator arm modeled through the Denavit Hartenberg convention executes precise tasks (medication delivery, tool positioning) with sub-millimeter repeatability. A dynamic energy-saving protocol reduces idle sensor and actuator power draw by ∼30%, extending overall system autonomy by 25% in hospital trials. By fusing adaptive navigation, robust AI analytics, and kinematic precision within an energy-aware design, this framework significantly enhances clinical responsiveness and durability of monitoring infrastructure, while alleviating caregiver workload. The proposed optimizations target four critical parameters: reducing energy consumption, minimizing execution time, lowering hardware costs, and controlling temperature increases in electronic circuits. Experimental results confirm that our optimized IoT node designed to IRPM system reliably monitors in real time while maintaining operational efficiency and extending the system’s lifespan. This makes it well-suited for sustainable, hospital-based healthcare environments.