Sport training action correction by using convolutional neural network

Abstract

This paper proposes a framework for correcting the actions during sport training. We estimate the positions of bone joint points and the postures by using a convolutional neural network. The positions of bone joint points and the postures are the premise to correct the actions during sport training. In convolutional neural network, it first predicts the positions of bone joint points via confidential map of different parts; then utilizes affinity domain, which is a 2‐dimensional vector set, to predict the directions and positions of four limbs; lastly repeats the above steps and analyzes the bone joint points via greedy analytical reasoning algorithm to construct the human skeleton. The personal action is represented as the constructed skeleton which is used to correct the incorrect or non‐standard actions during sports. The experimental results evaluate the effective of the proposed sport training action correction method.