IoT-Health: A Framework for Integrating IoT Devices for Monitoring Back Posture

In the modern era, healthcare monitoring has emerged as a critical aspect of preventive medicine, enabling early detection of health issues and timely intervention. The evolution of technologies ranging from wearable devices to remote sensing systems revolutionized healthcare by enabling continuous and non-invasive tracking of vital signs, activity levels, and physiological parameters. The wide adoption of healthcare monitoring technologies challenges the ability to integrate and analyze the high volume of data within multiple health care applications. This paper proposes a framework that integrates healthcare monitoring devices and the collected data for analysis. The proposed framework was validated by implementing a back posture monitoring application for early diagnosis and treatment. The collected data was analyzed through two different models: A time dependent model through Long Short-Term Memory (LSTM) and a time independent model using K-nearest neighbor (KNN). Both models showcased high prediction accuracies, with LSTM and KNN achieving a 99.2% and 92.9% accuracy rates, respectively.