Integrating Deep Learning With Near-Field IoT Sensing for Enhanced Patient Localization and Monitoring in Healthcare Facilities

In healthcare environments, accurate and real-time patient localization and monitoring are crucial for ensuring patient safety and improving operational efficiency. This article proposes DeepSense-Healthcare, a hybrid CNN-LSTM framework integrated with adaptive resource management to enhance near-field (NF) IoT-based patient localization and monitoring in healthcare facilities. By combining convolutional neural networks (CNNs) for spatial feature extraction with long short-term memory (LSTM) networks for temporal modeling, DeepSense-Healthcare captures complex spatial-temporal patterns in NF signals, achieving high localization accuracy. An adaptive resource management module is incorporated to optimize computational load, dynamically adjusting resource allocation based on patient activity levels, thereby improving energy efficiency and maintaining responsiveness. We evaluate the proposed framework against baseline models through extensive experiments on the SEED-VIG and ILM datasets across various activity levels. The results demonstrate that DeepSense-Healthcare outperforms conventional methods in localization accuracy, energy efficiency, and latency during various activity scenarios. These findings underscore the effectiveness of DeepSense-Healthcare as a robust and efficient solution for continuous patient monitoring in dynamic healthcare settings.