Abstract:
Worldwide, people struggle with low back pain. Monitoring the factors contributing to this pain is crucial to avoid such a medical disorder. Traditional methods consist of unreliable evaluations or delayed clinical tests to monitor and identify this health issue. At home, the patient is unable to determine the cause of the pain he/she is experiencing at the low back. A human-centered data collection was conducted at Rwamagana level-II teaching hospital and at RP/IPRC Gishari with the purpose of getting insights on prevalence of low back pain among employees and the key factors associated with this issue. The results from this survey showed that low back discomfort is more prevalent for nurses than the other employees asked with 30% and 16.7% respectively and this is explained by the fact that nurses are mostly engaged in activities that affect their low back. Existing solutions to this problem focused on gauging the muscle activity but did not consider that the pain at the low back can be caused by the bad posture of the low back. In this study, the IoT-based automatic monitoring and early warning system was designed and developed to address this gap. It is based on the principle of detecting the posture of the low back using an accelerometer sensor connected to the Node MCU which acts as the heart of the entire system. The posture data are sent into the database and accessed by the user through a web page. When the bad posture is detected for at least 30min the system warns the user to change that position and thus avoid the pain that may result. Circuit design in Proteus Software as well as prototype development are the approaches used to get the intended solution.
