Predictive Modeling of Chronic Pain Flares with Cloud-Enabled Wearable Sensors and Recurrent Neural Networks

Healthcare providers continue to face a major challenge in chronic pain treatment, which calls for creative solutions that can be implemented quickly. It provides a new approach to chronic pain flare prediction by mixing recurrent neural networks (RNNs) with wearable sensors communicating with the cloud. It created a model that can predict when pain will worsen by continuously monitoring physiological data, such as skin conductance, heart rate variability, and movement patterns, gathered by wearable sensors and fed into RNNs. It can accurately forecast the start of pain flares by using deep learning to examine complicated temporal patterns in sensor data. To provide proactive and individualized treatment of chronic pain, the cloud-enabled architecture allows for real-time analysis and smooth connection with current healthcare systems. By allowing preventative treatments and enhancing patients' quality of life, The promising results of our validation study demonstrate that our technique may revolutionise the treatment of chronic pain.