Title: Sports pose estimation based on adaptive complementary data fusion algorithm

Authors: Jinchi Yu; Xiaomin Gu

Addresses: Shenzhen Tourism College, Jinan University, Shenzhen, 513053, China ' Shenzhen Tourism College, Jinan University, Shenzhen, 513053, China

Abstract: Accurately estimating athlete pose is of great significance for improving training quality and preventing sports injuries. The current vision-based estimation methods are susceptible to environmental lighting interference, while inertial sensors suffer from cumulative errors. To optimise the accuracy and stability of athlete pose estimation, a motion pose estimation method based on adaptive complementary data fusion algorithm is proposed. A motion sensor data fusion algorithm based on Kalman filtering algorithm is designed to improve its accuracy. When the number of samples was 300, the pitch angle was 9°. When the number of samples was 200, the pitch angle was -9°. After UKF filtering, the maximum Roll angle was 20° and the minimum Roll angle was -85°. The Yaw angle error was reduced, with a maximum Yaw angle of 80° and a minimum Yaw angle of -100°. The experimental data proves the effectiveness and superiority of the proposed algorithm.

Keywords: Kalman filtering algorithm; pose estimation; athletic sports; adaptive algorithm; sensor data fusion.

DOI: 10.1504/IJCSM.2025.151193

International Journal of Computing Science and Mathematics, 2025 Vol.22 No.3, pp.209 - 226

Received: 20 Nov 2024
Accepted: 05 Jul 2025
Published online: 16 Jan 2026 *

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