Self-Management of Low Back Pain Using Neural Network

Low back pain(LBP)is a morbid condition that has afflicted several citizens in Europe.It has negatively impacted the European economy due to several man-days lost,with bed rest and forced inactivity being the usual LBP care and management steps.Direct models,which incorporate various regression analyses,have been executed for the investigation of this premise due to the simplicity of translation.However,such straight models fail to completely consider the impact of association brought about by a mix of nonlinear connections and autonomous factors.In this paper,we discuss a system that aids decision-making regarding the best-suited support system for LBP,allowing the individual to avail of reinforcement and improvement in its self-management.These activities are monitored with the help of a wearable sensor that helps in their detection and their classification as those that soothe or aggravate LBP and hence,should or should not be performed.This system helps the patients set their own boundaries and milestones with respect to suitable activities.This system also does windowing and feature extraction.The present study is an empirical and comparative analysis of the most suitable activities that patients suffering from low back pain can select.The evaluation shows that the system can distinguish between nine common daily activities effectively and helps self-monitor these activities for the efficient management of LBP.