Smart Cervical Band for Detection of Cervical Spondylosis using IoT Technologies

Wireless Body Area Networks, also known as WBANs, have the potential to become a new centre of research activity that provides exciting development prospects. This may happen if more people started using these networks, which are also known as WBANs. The frequency signal that is supported by non-contact sensing will give a solution to the problems of private comfort and privacy that have been an issue in recent years. The identification of changes in cervical mobility as well as changes in cervical strain over the course of time is an important part of both the diagnosis and the prevention of cervical spondylosis. In this article, we make use of channel state information to achieve sensible perception and observation, as well as the quick and cost-effective detection of various postures and inappropriate bending of the neck. These goals were accomplished by utilising channel state information. It provides a strategy that is both cost-effective and effective for protecting cervical health, and it also provides some assistance to medical professionals in learning the factors that contribute to cervical os sickness in a very timely manner. Both of these benefits are important in ensuring that cervical health is maintained. Caretakers and medical professionals pay particular attention to the warning indications of neck discomfort and cervical spondylosis (CS), two disorders that are common in individuals of all ages but are especially common in elderly people and small children. On the other hand, etiological variables such as hazardous posture, anxiety, depression, neck strain, and sports or activity activities that may develop moderate neck discomfort into serious surgical difficulties are typically not diagnosed in the early stages of the condition. These variables can develop relatively modest pain in the neck into more significant conditions that necessitate surgical treatment. In light of these concerns, a solution has been proposed that makes use of machine learning and is backed by the internet of things in order to lessen complications relating to neck pain and metallic components. In order to determine the position of the neck and send that information to a Google cloud server so that it may be recorded, the detector that is incorporated into a neckband has a gyro metre as well as a measuring instrument. In addition, alerts are connected to the user and sent to the user in the event that the user’s neck is in a position that is regarded to be abnormal. The accuracy of the system may be improved by employing machine learning algorithms that are in sync with the system that is advised.