Swimming Attitude for Tracking Error Correction Based on Mahony Algorithm

Abstract

Swimming is predominantly a long-distance endurance sport. In this sport, like in many others, monitoring and tracking swimming attitude error correction and how it changes over time is critical. Besides, in swimming posture measurement, due to the absorption and refraction of various signals of water, sensors relying on external information cannot provide accurate information. In addition, the inertial technology is not dependent on external information, suitable for the field of swimming posture measurement. Inertial attitude measurement requires initial alignment technology to provide initial values to calculate the attitude in the swimming movement. However, the swimmer’s jump time is uncertain and the traditional initial alignment algorithm needs a long time to get a high-precision result. To solve this problem, in this paper, we proposed a fast alignment method by the Mahony algorithm. In our work, we have used nine-axis inertial measurement unit of micro-electro-mechanical systems (MEMS) to collect information. Furthermore, we calculated the attitude angle by angular velocity information, horizontal attitude angle, and yaw angle, and these were corrected by acceleration information and magnetic field intensity information, respectively, and multisource information was integrated by a complementary filtering method. It is simple to acquire the starting value of inertial attitude measurement. Laboratory experiments verify that the horizontal accuracy of attitude angle can reach the angle classification within 3 s, which meets the requirements of swimming sports. The algorithm’s viability is further confirmed through experiments in real-world sporting scenarios.