Analysis and Guidance for Standing Broad Jump Based on Artificial Intelligence
Hao Yu, Dongfang Chen, Ye Tao, Bo Yu, Gang He · 2023
In this paper we propose and implements a standing long jump movements analysis algorithm based on artificial intelligence to help students improve their performance. We select the BlazePose, a lightweight neural network under the Mediapipe framework for human pose estimation. We extracte the coordinate information of body joints with Mediapipe, and use the extracted coordinate information to design the algorithm of standing long jump action segmentation and key frame selection for quantitative analysis movements. To evaluate the effectiveness of the project, we recruit 24 students to attend the Experiments with two rounds of tests, Experiment results show that the proposed algorithm can automatically detect and identify problems and deficiencies in key movements, and give suggestions for improvement. By analyzing the data of jumpers' take-off skills, posture control and landing methods, we can provide suggestions to help them improve their skills and performance.