Affiliations: [a] Teaching Department of Sports, Henan Polytechnic Institute, Nanyang, China | [b] School of Electronic Information Engineering, Henan Polytechnic Institute, Nanyang, China
Correspondence:
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Corresponding author: Yunjun Wang, Teaching Department of Sports, Henan Polytechnic Institute, Nanyang, China. E-mail: yunjunwangyj@163.com.
Abstract: Traditional standing long jump measurement relies only on visual reading and manual recording, which makes the recording of data subjective and arbitrary, making it difficult to ensure the accuracy and efficiency of long jump performance. To address the shortcomings and deficiencies of traditional measurement methods and to avoid the interference of subjective bias on results, the research aims to provide a more accurate, automated, and objective measurement method. Furthermore, the research will provide new technological means for the measurement of related sports projects. In contrast to the utilization of human motion recognition technology, the study introduces image recognition technology into the domain of standing long jump testing. This technology enables the calculation of distance through the application of image processing and perspective transformation algorithms, thereby facilitating the realization of a distance measurement function. Specifically, this includes using wavelet decomposition coefficients and morphological denoising to improve the performance of wavelet threshold denoising, achieving feature extraction of image edge information, adding vibration sensors and CNN algorithms to adjust the angle of offset images, and designing a multi-step long jump distance measurement system. The combination of wavelet decomposition coefficients and morphological denoising utilized in the study demonstrated lower mean square error (50.8369) and signal-to-noise ratio (24.1126) values, with a maximum accuracy of 96.23%, which was significantly higher than the other two comparison methods. In the context of different feature information recognition, the ROC curve area of the algorithm model proposed in the study reached over 85%, with a deviation in the dataset of all below 0.5. The minimum absolute and relative errors between the measurement results of this method and the actual test results were 0.01 cm and 2%, respectively. The overall deviation of the system was 0.35, indicating high stability. The proposed long jump measurement system has the potential to enhance the efficiency of testing for the standing long jump, while also forming a complementary mode with traditional distance measurement systems. This could collectively serve the intelligent instrument market, providing technical means for the development of sports teaching projects.
