Recognition of Anaerobic based on Machine Learning using Smart Watch Sensor Data

In recent years, there has been an upsurge in research on smart watch technology. Existing research and commercial applications for machines that recognize user behaviors have involved measuring aerobic exercise using physical displacement metrics rather than anaerobic exercise, which involves recognizing and measuring user behaviors using signal processing techniques or other instruments. In this paper, we have created a prototypical machine learning algorithm to measure anaerobic exercise with dumbbells to improve the recognition of physiologic markers of exercise. To do so, we have chosen three kinds of anaerobic exercise using dumbbells -pull-ups, side pulls, and concentration curls -to be monitored with a three-axis gyroscope sensor (motion sensor of a smart watch), a three-axis acceleration sensor, and a support vector machine (SVM) algorithm. Experimental results indicate a mean recognition rate of 97.7% with respect to the three kinds of exercise analyzed here.