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      A Survey of Vision-Based Human Action Evaluation Methods

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          Abstract

          The fields of human activity analysis have recently begun to diversify. Many researchers have taken much interest in developing action recognition or action prediction methods. The research on human action evaluation differs by aiming to design computation models and evaluation approaches for automatically assessing the quality of human actions. This line of study has become popular because of its explosively emerging real-world applications, such as physical rehabilitation, assistive living for elderly people, skill training on self-learning platforms, and sports activity scoring. This paper presents a comprehensive survey of approaches and techniques in action evaluation research, including motion detection and preprocessing using skeleton data, handcrafted feature representation methods, and deep learning-based feature representation methods. The benchmark datasets from this research field and some evaluation criteria employed to validate the algorithms’ performance are introduced. Finally, the authors present several promising future directions for further studies.

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          Most cited references 127

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          A survey on vision-based human action recognition

           Ronald Poppe (2010)
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            Human activity analysis

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              Behavior Recognition via Sparse Spatio-Temporal Features

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                24 September 2019
                October 2019
                : 19
                : 19
                Affiliations
                [1 ]Department of Computer Science and Technology, Huaqiao University, Xiamen 361000, China; zhanghongbo@ 123456hqu.edu.cn (H.-B.Z.); shuangy_amoy@ 123456163.com (S.Y.); dschen@ 123456hqu.edu.cn (D.-S.C.)
                [2 ]Xiamen Key Laboratory of Computer Vision and Pattern Recognition, Huaqiao University, Xiamen 361000, China
                Author notes
                [* ]Correspondence: leiqing@ 123456hqu.edu.cn (Q.L.); jxdu@ 123456hqu.edu.cn (J.-X.D.)
                Article
                sensors-19-04129
                10.3390/s19194129
                6806217
                31554229
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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