Human body posture recognition has far-reaching significance in the fields of human-computer interaction, games, and medical health. It is a difficult research point in this field to perform high-precision and stable recognition of various human body posture based on portable sensors. This study collects high-frequency sensor data of eight postures, and the data set is sorted out by extracting the window time-domain features of the original data. According to the characteristics of the sensor data, the human posture is divided into four stages, and the Gaussian Mixture Model (GMM) is used to fit the observation sequence of the human posture, combined with the Hidden Markov Model (HMM), then, use GMM-HMM algorithm for gesture recognition. This study compares the effects of the First Order Hidden Markov Model (1OHMM) and the Second Order Hidden Markov Model (2OHMM) under different window values. When the window value is 8, the performance of 2OHMM is optimal, and the overall recall rate reaches 95.30%, the average accuracy rate reaches 95.23%. Compared with other studies, the algorithm in this work can recognize more types of gestures, has better recognition performance, and takes less time.