To address the issues of slow efficiency,low accuracy,and poor robustness associated with traditional posture estimation techniques,we propose an algorithm based on Mahony-EKF fusion and develop a new human arm posture estimation technique.First,collect the data measured by MEMS sensors using an STM32 microprocessor,and solve the accelerometer,magnetometer and gyroscope data using the Mahony filter to obtain the preliminary quaternion of body attitude.Next,based on the non-gravitational acceleration,the preliminary quaternion is used as the EKF measurement and adjust the measurement matrix of noise.Then the EKF state equation is established based on the angular velocity information measured by the gyroscope,and the state is updated by EKF filtering to obtain the solved and fused attitude data of the arm.Finally,the data are sent to the host computer,the angle data of attitude are monitored in real time by the software itself and the motion state of the arm is restored in real-time by constructing the 3D model.After the experimental verification,utilize the EKF algorithm to correct the attitude data solved by Mahony filtering which can not only reduce the error to 0.5°,eliminate the overshoot and reduce the noise interference but also effectively overcome the problem of requiring a large number of data sets and long computation time in the traditional methods,to suppress the random fluctuation and improve the accuracies.