IoT-Driven Posture Correction for Chronic Back Pain Mitigation Using Bayesian Networks

Poor posture is a common cause of chronic back pain, a major health issue. This research delves into an Internet of Things (IoT)-driven posture correction system that uses Bayesian Networks (BN) for data analysis and decision-making to alleviate chronic back pain. Incorporating IoT sensors into a person's clothing allows us to track their sitting behavior and other real-time posture indicators. BN are used to evaluate the potential of posture-related dangers and pain. The Bayesian model incorporates past data and current inputs to provide accurate predictions and personalized recommendations for posture correction. Through its easy user interface, the technology provides users with useful input that they can utilize to improve their posture in real time. The system's efficacy was tested in clinical studies, which showed that participants' posture improved significantly, and their back pain symptoms decreased. The results of this study show that a data-driven strategy for chronic back pain management is possible when IoT technology is combined with BN. Our system utilizes advanced data analysis and continuous monitoring to enhance spinal health and general well-being proactively.