This paper presents a method based on MPU9250 microprocessor for human motion recognition. The user performs various types of sports while wearing the bracelet, and the bracelet automatically collects and stores the acceleration data generated by the user during the movement. Analysis of these data can identify the type of human motion. The acceleration of the motion in the X, Y, and Z directions is collected by the acceleration sensor embedded in the wristband. After filtering by the filtering algorithm, the data is analyzed in the time domain and the frequency domain respectively, and then subjected to feature engineering extraction. There are 34 related features, feature selection algorithm is used to select the 16 main features, reducing the complexity of the algorithm. Experiments compare the three methods, namely Support Vector Machine (SVM), decision tree, and random forest, in classification of five sports, modes such as walking, running, badminton forehand swing, table tennis, and rowing. The results show that the random forest has the best accuracy which can reach more than 97%.