Abstract

Human Pose Estimation (HPE) has received considerable attention during the past years, improving its performance thanks to the use of Deep Learning, and introducing new interesting uses, such as its application in Sport and Physical Exercise (SPE). The aim of this systematic review is to analyze the literature related to the application of HPE in SPE, the available data, methods, performance, opportunities, and challenges. One reviewer applied different inclusion and exclusion criteria, as well as quality metrics, to perform the paper filtering through the paper databases. The Association for Computing Machinery Digital Library, Web of Science, and dblp included more than 500 related papers after the initial filtering, finally resulting in 20. In addition, research was carried out regarding the publicly available data related to this topic. It can be concluded that even if related public data can be found, much more data is needed to be able to obtain good performance in different contexts. In relation with the methods of the authors, the use of general purpose systems as base, such as Openpose, combined with other methods and adaptations to the specific use case can be found. Finally, the limitations, opportunities, and challenges are presented.

Highlights

  • Human Pose Estimation (HPE) consists of estimating the position of different parts of the body, such as the joints in a 2D or 3D space depending on the estimation type, normally from visual information, such as images, and sometimes through other additional data obtained by different types of sensors, such as inertial sensors or depth sensors

  • The first one is focused on monocular approaches, while the second survey gives an overall view of the different types of HPE systems, such as 2D and 3D, single view and multi-view, single person, and multi-person, and so on

  • Can we find a variety of sports in which HPE has been applied?

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Summary

Introduction

Human Pose Estimation (HPE) consists of estimating the position of different parts of the body, such as the joints in a 2D or 3D space depending on the estimation type, normally from visual information, such as images, and sometimes through other additional data obtained by different types of sensors, such as inertial sensors or depth sensors. This field of research can be considered a combination of Data Processing and Artificial Intelligence, Computer Vision. A view of the available public datasets, as well as the used metrics, is presented as well

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