Smart Bed Monitoring System for Predictive patient care: A Systematic Review of Sensor Technologies and Ai Approaches

Abstract

Abstract: The need to constantly and remotely monitor patients has led to considerable progress in smart bed-based healthcare. This paper includes the systematic review of the assessment of intelligent bed activity using vital signs sensors with emphasis on the combination of sensor technologies and machine learning methods for non-contact physiological monitoring. The search, filtering and reviewing of relevant scientific literature was carried out by systematically searching and screening literature in key scientific databases. The review analyses main monitoring parameters like heart rate, respiration and sleep patterns, the nature of intelligent bed system like the type of sensors used, their setup and integration plan. It also provides an evaluation of machine learning models, from classic algorithms to new advanced machine learning models based on deep learning, for interpreting data and evaluating health. The results indicate that although deep learning methods provide better performance, issues like data scarcity and real-life validation are still major constraints towards real-world implementation. The paper concludes with research gaps of significance and future prospects to enhance system reliability, scalability and clinical applicability to achieve next-generation smart healthcare monitoring solutions.