WSN-Driven Posture Recognition and Correction Towards Basketball Exercise

Abstract

In order to enhance the daily training for basketball sport, this paper establishes a human posture estimation framework by using monocular camera and wireless sensor network. First, the daily basketball training images are collected by monocular camera and transmitted through wireless sensor network. Second, the collected images are processed by an observation and reasoning model which is based on component and graph reasoning. The basketball player’s posture is depicted by the rotation invariant features of edge field. The extracted features are used to learn a boosting classifier as the observation model. The experimental results show that the posture recognition rate can achieve more than 88% for basketball player’s action.

High Speed and Accuracy of Animation 3D Pose Recognition Based on an Improved Deep Convolution Neural Network

“…For example, Simonyan et al (2014) proposed a scheme based on a feedforward neural network that uses neural networks to directly estimate the corresponding 3D pose from the extracted 2D pose [7]. Based on similar schemes, Facebook AI introduced temporal series information to further improve the pose performance [8], which directly uses images as inputs. Ji et al (2022) proposed the encoder-decoder network (EDN) model based on a given image [9].…”