A novel wireless sensor network deployment for monitoring and predicting abnormal actions in medical environment and patient health state

Wireless Sensor Networks (WSNs) play a crucial role in modern healthcare applications by sensing and communicating environmental and patient health data through wireless mediums. These networks enable real-time monitoring of patients, including the elderly and individuals with chronic conditions, offering significant benefits in surveillance and healthcare management. However, several challenges persist in implementing WSNs for healthcare. Real-time administration faces obstacles such as managing patients, medical infrastructure, and staff while ensuring timely treatment delivery. Moreover, accurately predicting diseases and providing precise treatment remains complex due to the diverse nature of medical data and the limitations of traditional systems. This study proposes a deep learning-based approach integrated with WSNs to address these challenges. The system employs deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) models to automate healthcare functions such as patient monitoring, infrastructure management, and disease prediction. The proposed model (DCNN-LSTM) analyzes diverse data types, including numeric, alphanumeric, images, signals, and video, to accurately identify abnormal conditions and predict diseases. The system also generates real-time alerts for medical personnel, ensuring timely intervention. The proposed framework was validated using NS2 simulation software to model the WSN architecture and sensor node communication. Experimental results demonstrate that the DCNN-LSTM models achieved a classification accuracy of 96 % with a loss rate of 0.08, showcasing significant improvements over traditional approaches. Integrating WSNs and deep learning enhances real-time healthcare monitoring, improving efficiency and patient outcomes.