Research And Application Of Long-distance Running Posture Based On Inertial Sensor

Due to the rapid development of the national economy and the improvement of material living standards,the people’s awareness of sports health has also become more and more important.Long-distance running is an efficient and simple exercise method,which can effectively improve the function of the athlete’s respiratory system and heart and blood system,and has the functions of promoting their body metabolism,soothing the body and mind,and regulating the mood.The wrong running posture not only fails to exercise the body effectively,but also easily causes more serious damage to the body.The main methods of motion gesture recognition include image analysis and recognition and inertial sensor detection.With the advantages of high precision,high sensitivity,and small size,the attitude detection method based on inertial sensors is widely used in sports and healthy wearable devices,and has become the trend of smart device development today.This paper introduces the theory of longdistance running kinematics.Based on the research of posture detection and recognition using inertial sensors,a long-distance running foot posture detection and upper body posture detection algorithms based on inertial sensors are designed,and an analysis and evaluation method for longdistance running posture is established.First of all,in view of the high-intensity exercise in long-distance running,the accuracy of foot posture detection is reduced.This paper uses MPU9250 inertial sensor to design an adaptive error quaternion unscented Kalman filter algorithm(DAUKF).The algorithm uses the error quaternion and the gyroscope drift error to establish the state equation,the accelerometer and magnetometer measurement values establish the observation equation,and introduces the fading memory method to adaptively adjust the observation noise covariance to reduce the system itself and the environment’s attitude Interference detected.Experiments show that this method improves the accuracy of posture detection,effectively suppresses the effects of drift errors and dynamic observation noise,and provides a foot posture detection scheme suitable for longdistance running.Secondly,considering the motion characteristics of the upper body posture,the multiplicative extended Kalman filter algorithm(MEKF)based on quaternion is improved.After experimental analysis,compared with the general complementary filtering method,this algorithm improves the detection accuracy of long-distance running posture.The MARG sensor is used to collect the upper body posture in long-distance running,which further improves the method of judging running posture only from the foot posture.Finally,use the above two posture detection algorithms for foot posture and upper body posture to conduct experiments and propose a long-distance running posture evaluation method.After collecting the foot posture and upper body posture signals and calculating the posture angle data,the long-distance running posture analysis is performed Evaluation,to get a long-distance running posture evaluation combining the two parts of the foot and the upper body.