Human body flexibility fitness test based on image edge detection and feature point extraction
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Human body flexibility fitness test based on image edge detection and feature point extraction Xu Lu1 · Yujing Zhang1
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This paper proposes a human flexibility fitness detection algorithm based on edge detection and feature point extraction. This algorithm first improves on the deficiency of the classical Canny operator. Specifically, a hybrid filter is used instead of the original Gaussian filter to improve filtering performance. Next, the templates in the 45◦ and 135◦ directions are added based on the original gradient calculation templates, and Otsu algorithm is used to achieve threshold segmentation to obtain the final edge information. Then, based on the obtained edge information, a human body feature point extraction algorithm for calculating the anteflexion angle is proposed, and the feature points of the shoulder, hip, and leg of the person are extracted, and the angle formed by these points is calculated. Size is used to achieve human body flexibility and fitness testing. In order to verify the effectiveness of the edge detection algorithm proposed in this paper, experiments are performed to compare with other algorithms, and the results show that the results of our algorithm are more accurate. Keywords Physical flexibility fitness · Human body anteflexion angle · Edge detection · Feature point extraction
1 Introduction Physical fitness is interpreted as a measure of the ability to perform a physical activity that integrates the majority of bodily functions; its components are divided into those related to health and those related to athletic skill (Lu et al. 2019). As a part of physical fitness, physical flexibility fitness, which plays an increasingly important role in our daily life, is a vital indicator of human health. In the daily health fitness test, the fitness is measured mainly through the sit-and-reach (SR) test (Mookerjee and McMahon 2014). And the final test results are based on the longest distance that the subjects fingers have reached forward (Youdas et al. 2008). Actually, the measurement of human flexibility is mainly achieved by measuring the anteflexion angle of the human body. However, the accuracy of the SR test results is usually affected by the length of the limb. What is worse, the traditional measurement method requires using equipment, thereby increasing measurement costs. Communicated by A. Di Nola.
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Xu Lu [email protected]
With the development of computer technology, image processing technology has been applied to numerous aspects of daily life, such as astronomy (Farrens et al. 2017), satellite detection (Segl and Kaufmann 2001), security (Ziad et al. 2016), and medical and health (Razzak et al. 2018). And there are more and more occasions for processing human body images, for example, action recognition (Yao et al. 2015), video tracking (Zhang et al. 2015), and people identification (Zhang et al. 2017). Inspired by this, we proposed a human flexibility fitness detection algorithm based on ed
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