Algorithm for Swimmers’ Starting Posture Correction Based on Kinect

Abstract

Effective identification and correction of swimmers’ improper postures can significantly improve athletes’ weekday swimming training quality. The human body’s affine deformation is prone to occur during swimming movements when performing posture recognition and correction, resulting in the creation of low-brightness action feature locations. The inability of coaches to identify and correct athletes’ improper posture in real time is a result of a lack of detection and correction. Additionally, the human skeleton motion data from the depth camera Kinect contains a high amount of noise and fewer skeleton nodes, and the data level of detail is low. To overcome this issue, this research proposes a network for enhancing Kinect skeleton motion data. The network is composed of six bidirectional cyclic autoencoder stacks. The stacking structure improves the smoothness and naturalness of the data, and the training phase includes hidden variable limitations to ensure that the bone motion data preserve a genuine bone shape when the degree of detail is raised. The trials demonstrate that the optimized data from the network have a better degree of smoothness and can keep a more realistic bone structure, enabling the goal of obtaining high-precision motion capture data with low-precision Kinect equipment to be met.