| Title |
Application of Posture Estimation Algorithm Based on Extended Kalman Filter in Sports Action Recognition |
| DOI |
https://doi.org/10.5573/IEIESPC.2025.14.2.229 |
| Keywords |
EKF; Attitude estimation; Action recognition; Sports; MEMS |
| Abstract |
In response to the shortcomings of low recognition accuracy and long recognition time in current sports action recognition models, this study combines extended Kalman filtering and microelectromechanical system sensors to build a new sports action recognition model. Firstly, the full angle pose calculation method is introduced to optimize the extended Kalman filtering algorithm. Then, the optimized pose estimation algorithm is combined with microelectromechanical system sensors to build the final sports motion recognition model. The research results indicated that the estimation error of the optimized attitude estimation algorithm was as low as 0.01. The motion recognition model constructed had high accuracy rates of 0.99, 0.98, and 0.98 for recognizing serve, drop, and spike movements in badminton, with a time consumption of 2.01 s, 1.88 s, and 1.96 s, respectively, demonstrating good recognition performance. The above results indicate that the attitude estimation algorithm and recognition model designed in this study have good performance and practical application effects, and can provide new reference methods for intelligent recognition of sports. |