Exercise abnormality detection using BlazePose skeleton reconstruction

Abstract

There still exists a knowledge gap in the field of computer vision in respect of posture prediction and deviation evaluation is an important metric for various medical applications, that require posture abnormality quantization. Our paper proposes a deep heuristic neural network architecture, using BlazePose as a backbone, that is capable of reconstructing users skeleton from a real-time monocular video feed, using which we are able to evaluate the subjects performed exercise and measure the deviation from expected values. The proposed heuristics are able to identify and evaluate most of the abnormalities, with the highest indicator of postural issues being the spinal deviation accounting for 95%. Additional evaluation of real-time performance has shown that our method is capable of maintaining 23-ms response times, making it applicable to real-time applications. © 2021, Springer Nature Switzerland AG.