Research on running posture recognition based on bone node detection

Juan Liu; Wanli Zhang

doi:10.1117/12.3029143

6 May 2024 Research on running posture recognition based on bone node detection

Juan Liu, Wanli Zhang

Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 131073F (2024) https://doi.org/10.1117/12.3029143
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China

Abstract

As a simple and effective way of exercise, running is favored by people. At the same time, people are paying more and more attention to the correctness and standardization of running posture. Traditional posture recognition methods rely on the experience of professional coaches and manual evaluation, but this method is not only time-consuming and laborious, but also subjective. In response to this problem, this study aims to explore a running posture recognition method based on bone node detection technology to provide accurate and real-time posture evaluation for runners. First, by studying the runner's running video, and using deep learning methods to detect bone nodes. Then, a runner's posture recognition model is established to identify different running postures by analyzing the spatial relationship and dynamic characteristics between skeletal nodes. The experimental results show that the method can identify the running posture efficiently and accurately with a variety of latest methods on multiple data sets. This study provides a feasible posture assessment method for runners, which is of great significance for improving running effect and preventing running injury.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Juan Liu and Wanli Zhang "Research on running posture recognition based on bone node detection", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 131073F (6 May 2024); https://doi.org/10.1117/12.3029143

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KEYWORDS

Detection and tracking algorithms

Bone

Data modeling

Data processing

Databases

Machine learning

Video processing

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