DEVELOPMENT OF AN EMBEDDED EXERCISE POSTURE PREDICTION SYSTEM FOR OFFICE SYNDROME USING MACHINE LEARNING

Office syndrome is a condition with a high prevalence among people of working age and an upward tendency. In this study, a wearable device implant based on machine learning will be created for exercise of office syndrome. Developing a machine learning model that can distinguish 3 exercise postures for implantable devices was the objective of the study. The Edge Impulse online software was used for data collecting, preprocessing, training, and testing. After that, the model was successfully installed and tested on an Arduino Nano 33 BLE microcontroller. In addition to designing embedded systems, this work also developed applications through the App Inventor program for exercising properly. From testing the system, the model showed an average classification accuracy of 97.6% of exercise postures. The results of testing the embedded system in conjunction with the presented application on 10 subjects showed that the system was able to provide predictive accuracy for wrist exercises, shoulder exercises and stretch exercises with 95%, 97%, and 93%, respectively.