In order to achieve rapid gait state judgment and analysis to better perform highprecision gait recognition and control of the exoskeleton of the lower limbs, the algorithm research based on wearable inertial measurement device to detect human body posture change is carried out. Through the measurement experiment of nonperiodical gait changes such as falling, turning, squatting and standing up of the lower limbs of the human body, data of the subjects’ body angle, lower limb joint angular velocity and acceleration changes during the experiment were obtained, and then random forests were applied. Four classic classification algorithms for machine learning have performed a comparative analysis of activity recognition on subjects. The results show that compared with other algorithms, the decision tree supervised learning algorithm can quickly and accurately detect and judge a variety of nonperiodic changes in the human body in the active state, the previous recognition accuracy can reach more than 99%. Research can provide a theoretical basis for the development and application of wearable smart equipment.