Physical education teaching in colleges and universities faces the problems of lack of attraction, no daily exercise supervision, and teachers cannot accurately grasp the exercise intensity of each student. In this paper, in order to innovate the sports teaching mode in colleges and universities, a sports monitoring system in colleges and universities is designed, which firstly expresses and calculates the motion features in space, updates the normalized quaternion, and obtains the real-time posture angle of the human body. The motion feature extraction is carried out to accurately fit the trend of motion state change after Kalman filtering of the state quantity to monitor stationary, running, fast walking, and warm-up motion recognition. The monitoring system that was constructed is utilized in a college sports classroom, and the waveforms of every motion signal measured by the system are examined. The innovative model of college sports was tested in a one-semester teaching experiment. It is found that the system determines that the combined acceleration of a student at rest is 1.005g to 1.03g, the number of times that the combined acceleration peak is greater than 2g and less than 6g when running 28 steps is 28 times, and the combined acceleration of fast walking 12 steps meets the judgment criteria for a total of 12 times, which is in line with the actual state of the system. After one semester of an innovative mode teaching control experiment, the average physical education score of the experimental class increased dramatically from 60.8 points to 78.6 points, an increase of 29.3%. This study provides a useful exploration of the innovation of physical education teaching methods in colleges and universities, taking into account the integration of big data and information technology in physical education.