本文著录格式：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.
摘要：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.
关键词：Posture recognition; artificial intelligence; machine learning; deep neural network; deep learning; transfer learning; feature extraction; classification
全文下载：
A Survey on Artificial Intelligence in Posture Recognition.pdf
本期封面
期刊介绍:
CMES-Computer Modeling in Engineering & Sciences创刊于2000年，月刊，由Tech Science Press出版商出版，期刊被SCIE收录，WOS分区为二区。
CMES-Computer Modeling in Engineering & Sciences是一本专注于工程与科学领域的计算建模学术期刊，旨在报道计算方法在工程与科学领域的应用、发展和创新。期刊主要关注计算力学、计算物理、计算化学和计算生物学等领域具有价值的原创研究性论文，对各种空间尺度如量子、纳米、微观、宏观等或者是各种时间尺度如从皮秒到小时的研究比较有兴趣，特别鼓励涉及多物理场问题的论文，以及涉及力学、化学和生物学界面的，或者是更有效的算法等研究论文。
投稿方式（网址）：https://www.techscience.com/journal/CMES
作者简介:
蒋小艳，现为南京特殊教育师范学院数学与信息科学学院副教授，江苏省高校“青蓝工程”优秀青年骨干教师培养对象。主要从事智能图像处理、残疾人教育康复工程等研究。主持研究全国教育科学“十三五”规划教育部重点立项课题1项、江苏省高校哲学社会科学重大项目1项，江苏省教育科学规划重点课题1项、江苏省高校自然科学研究面上项目1项、江苏省高校哲学社会科学基金资助项目1项、江苏省社科应用研究精品工程课题1项、江苏省教改立项课题1项；参与研究省厅级课题多项；公开发表论文30余篇。
（供稿/倪盈盈 审核/张伟锋）