Real-Time Monitoring of Patient Movement and Restraint Usage in healthcare Settings with IoT and Deep Learning Model

Ensuring patient safety and maximizing care delivery in healthcare settings requires effective monitoring of patient movement and restraint usage. To address this challenge, this paper proposes a real-time monitoring system that combines Internet of Things (IoT) technology with a Deep Learning (DL) Model, specifically Neural Networks. Utilizing an IoT sensor network, the system regularly gathers and sends data on patient movements and instances of regular application of restraint. The primary innovation is applying neural networks to the real-time recognition and classification of movement patterns and restraint events. This methodology allows the system to discriminate between different movement types (e.g., walking, sitting, and lying down) and precisely detect when a restraint is used. Healthcare professionals can obtain important insights into patient behavior and the efficacy of restraint techniques through real-time analysis of these data streams. Intelligent monitoring and analysis of the proposed system will increase the effectiveness of using restraints, thus improving patient care. Healthcare providers can reduce the risks associated with the inappropriate use of restraints and develop a safer environment by predicting patients' needs and responding proactively with timely alerts and actionable insights. It advances healthcare technology for real-time patient monitoring and safety management in healthcare settings.