Deploying Healthcare Monitoring System For Elderly Patient Care using IoT and Neural Network Techniques
Jagendra Singh, Neha Garg, Ravikumar Sethuraman, Soumi Ghosh, Ajit Kumar, Raju Kolluri · 2024
Utilizing Internet of Things (IoT) technology in the healthcare sector can revolutionize the care provided to individuals. This study illustrates the application of IoT in ensuring continuous healthcare for elderly patients, particularly focusing on scenarios like epidemiology in rehabilitation centers, intensive care units (ICU), and home health care. The research integrates a variety of sensors, machine learning algorithms, and cloud computing to establish a healthcare system driven by IoT. With a participant pool of 50 individuals, including both healthy adults and those with various ailments, the study employs Short-Term Memory (LSTM), decision trees, and Artificial Neural Network (ANN) models to analyze sensor data, specifically temperature and heart rate measurements. The primary objective is to identify uncertainties and facilitate timely interventions. Results indicate that ANN surpasses LSTM and tree models in parameter estimation, displaying superior accuracy and F1 scores in anomaly detection while minimizing false negatives. This use of predictive ability has the chance of reforming treatment through early detection and administration of treatments. This research, in short, exemplifies the importance of integrating prognostic analytics in health systems that support IoT; in other words, it enables continuous monitoring, forecasting, and access to care requirements concerning changing forms of illness. From a scientific perspective, this work is seen as being proactive because it adopts green medical materials and uses IoT-based methods so that control performance may be improved for the patients.