Posture detection of athletes in sports based on posture solving algorithms

Abstract

With the rapid development of science and technology, the field of sports is constantly exploring and applying new technical means to improve the training effect and competitive level of athletes. Among them, the athletes' posture detection technology based on the attitude solving algorithm has been widely concerned in recent years. However, the current attitude solving algorithm has the limitation of low precision and low efficiency. Aiming at this, a new attitude solving algorithm is proposed. Firstly, the coordinate system is determined according to the theory of inertial navigation, and the attitude Angle is obtained by calculating the acceleration and magnetic induction intensity. Then the current attitude matrix is calculated according to the obtained attitude Angle. The initializing quaternion based on the attitude matrix is studied. Then, according to the advantages and defects of the three sensors, a complementary filtering algorithm is proposed for data fusion, so as to reduce the error of the final attitude solution. In order to further improve the accuracy of attitude detection, the complementary filter algorithm and double-layer Kalman filter algorithm are combined to process the data, and finally the quaternion is updated. It can be seen that the detection error of the research constructed model is only 9.94%, and its three attitude angle errors are mainly concentrated between -0.5° and 0.5° The model constructed by the research can realize high-precision posture detection, which can provide more scientific and reliable training aids for gymnastics, which has very strict requirements for movements in sports. It has positive significance for the development of sports.