Open Access
REVIEW
A Survey on Artificial Intelligence in Posture Recognition
Xiaoyan Jiang1,2, Zuojin Hu1, Shuihua Wang2, Yudong Zhang2,*
1
Nanjing Normal University of Special Education, Nanjing, 210038, China
2
School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK
* Corresponding Author: Yudong Zhang. Email:
Computer Modeling in Engineering & Sciences 2023, 137(1), 35-82. https://doi.org/10.32604/cmes.2023.027676
Received 08 December 2022; Accepted 05 January 2023; Issue published 23 April 2023
Abstract
Over the years, the continuous development of new technology has promoted research in the field of posture
recognition and also made the application field of posture recognition have been greatly expanded. The purpose
of this paper is to introduce the latest methods of posture recognition and review the various techniques and
algorithms of posture recognition in recent years, such as scale-invariant feature transform, histogram of oriented
gradients, support vector machine (SVM), Gaussian mixture model, dynamic time warping, hidden Markov model
(HMM), lightweight network, convolutional neural network (CNN). We also investigate improved methods of
CNN, such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and
high-resolution nets. The general process and datasets of posture recognition are analyzed and summarized, and
several improved CNN methods and three main recognition techniques are compared. In addition, the applications
of advanced neural networks in posture recognition, such as transfer learning, ensemble learning, graph neural
networks, and explainable deep neural networks, are introduced. It was found that CNN has achieved great success
in posture recognition and is favored by researchers. Still, a more in-depth research is needed in feature extraction,
information fusion, and other aspects. Among classification methods, HMM and SVM are the most widely used,
and lightweight network gradually attracts the attention of researchers. In addition, due to the lack of 3D benchmark
data sets, data generation is a critical research direction.
Keywords
Cite This Article
Jiang, X., Hu, Z., Wang, S., Zhang, Y. (2023). A Survey on Artificial Intelligence in Posture Recognition.
CMES-Computer Modeling in Engineering & Sciences, 137(1), 35–82. https://doi.org/10.32604/cmes.2023.027676