Xing Mengmeng, Wei Guohui, Liu Jing, Zhang Junzhong, Yang Feng, Cao Hui
School of Science and technology, Shandong University of Traditional Chinese Medicine, Jinan 250355, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2020 Feb 25;37(1):174-178. doi: 10.7507/1001-5515.201906053.
Human motion recognition (HAR) is the technological base of intelligent medical treatment, sports training, video monitoring and many other fields, and it has been widely concerned by all walks of life. This paper summarized the progress and significance of HAR research, which includes two processes: action capture and action classification based on deep learning. Firstly, the paper introduced in detail three mainstream methods of action capture: video-based, depth camera-based and inertial sensor-based. The commonly used action data sets were also listed. Secondly, the realization of HAR based on deep learning was described in two aspects, including automatic feature extraction and multi-modal feature fusion. The realization of training monitoring and simulative training with HAR in orthopedic rehabilitation training was also introduced. Finally, it discussed precise motion capture and multi-modal feature fusion of HAR, as well as the key points and difficulties of HAR application in orthopedic rehabilitation training. This article summarized the above contents to quickly guide researchers to understand the current status of HAR research and its application in orthopedic rehabilitation training.
人体运动识别(HAR)是智能医疗、运动训练、视频监控等众多领域的技术基础,受到了各界的广泛关注。本文总结了HAR研究的进展和意义,其包括两个过程:基于深度学习的动作捕捉和动作分类。首先,本文详细介绍了三种主流的动作捕捉方法:基于视频的、基于深度相机的和基于惯性传感器的。还列出了常用的动作数据集。其次,从自动特征提取和多模态特征融合两个方面描述了基于深度学习的HAR的实现。还介绍了在骨科康复训练中利用HAR实现训练监测和模拟训练。最后,讨论了HAR的精确运动捕捉和多模态特征融合,以及HAR在骨科康复训练应用中的关键点和难点。本文总结上述内容,以快速引导研究人员了解HAR研究现状及其在骨科康复训练中的应用。