Intellectual Rooms based on AmI and IoT technologies - Delhibabu, Radhakrishnan; Tuan Anh, Pham; Zhukova, Nataly; Subbotin, Alexey (Frontiers in Computer Science, 2025)
IntroductionThere is a growing need for advanced systems to monitor patients in both hospital and home settings, but existing solutions are often costly and require specialized hardware. This article presents a method for building “Intellectual Rooms,” a cost-effective and intelligent environment based on Ambient Intelligence (AmI) and Internet of Things (IoT) technologies. The objective is to enhance patient care by using machine learning to process data from readily available devices.MethodsWe developed a method for creating Intellectual Rooms that utilize a complex model integrating medical domain knowledge with machine learning for image processing. To ensure cost-effectiveness, data were gathered from various sources including public cameras, smartphones, and medical sensors. Machine learning tasks were distributed across edge devices, fog, and cloud platforms based on technical constraints. The system's effectiveness was evaluated using simulated test data representing various patient scenarios and abnormal actions, comparing four conditions with incrementally added data sources (public camera, smartphone, sensors, and environmental objects).ResultsThe system demonstrated a significant increase in the speed of providing assistance, with an average improvement of over 25%. The integration of multiple data sources progressively reduced false alarms: adding smartphone camera data reduced false alarms by 6.15%, incorporating sensor data led to a further 5.82% reduction, and considering surrounding objects achieved an additional 6.52% reduction. The cumulative effect of using all data sources resulted in a 23% overall improvement in the accuracy of identifying patient states.DiscussionThe results validate that the proposed method for building Intellectual Rooms is a feasible and effective approach for patient monitoring. By leveraging existing, low-cost hardware, the system offers a non-intrusive and intelligent solution suitable for both hospitals and home care. This study successfully demonstrated the core functionalities using simulated data; future work will involve deployment and evaluation in real-world clinical environments to confirm its practical utility.