Reinforcement Learning for Patient-Centric Lighting Management System in Healthcare Sector

This research study proposes a novel approach to enhance hospital lighting management by implementing dynamic lighting control. Traditional lighting systems often fail to meet the specific needs of patients, potentially impacting their health and well-being. The proposed system integrates the Internet of Things (IoT) and Reinforcement Learning (RL) algorithms to continuously track environmental factors and patient input. By analyzing this data, the system can make real-time adjustments to lighting characteristics, including intensity, color temperature, and spatial distribution. The proposed system’s autonomous optimization of lighting settings aims to promote patient comfort, mood, and overall well-being. Simulation results demonstrate the effectiveness and practicality of this approach in revolutionizing hospital lighting control. By prioritizing patients and focusing on individual needs, this research contributes to the development of healthcare facilities that enhance patient experience and promote healing.