Hospital Bed Vacancy Detection using Depthwise Separable CNN

The rising demand for real-time hospital bed man-agement in healthcare institutions requires creative solutions to improve patient care and resource allocation. This research introduces an IoT-enabled system for detecting hospital bed vacancies utilising a Depthwise Separable Convolutional Neural Network (DSCNN). The proposed system utilises IoT sensors to perpetually monitor bed occupancy, guaranteeing precise and prompt updates to medical personnel. The DSCNN is utilised to diminish computing complexity while preserving high detection accuracy, attaining a notable accuracy of 92.4%. The model's performance is assessed in comparison to traditional CNNs, revealing notable enhancements in both speed and accuracy, with accelerated convergence times enabled by the Adam optimiser. The system guarantees minimal latency in hospital bed detection, rendering it suitable for real-time applications on resource-limited devices. This system provides a scalable and effective method for optimising hospital resource management, especially during high demand or emergencies.