A long-term study of the effect of smart device use on student fitness in a university physical education programme

Physical fitness is a prominent problem that hinders the further development and improvement of China’s sports level, and as students in colleges and universities who bear the responsibility of cultivating future Chinese sports reserves, their physical fitness cannot be ignored. In this study, the intelligent sensing devices worn by the students are used to collect their movement data in physical education courses, and the high-frequency noise generated in the process of posture angle solving is suppressed by using high and low-pass complementary filtering algorithms and PI proportional-integral controllers. It also combines the skeletal point and space vector method to constrain the motion tracking model and subsequently uses the intelligent system to achieve the analysis of physical training data in sports courses with the help of training plan development. The long-term experimental results showed that after 24 weeks, the running speed of university women who used the smart device for physical education courses increased significantly, and the 50-meter running performance was reduced from 10.06±2.42s before the experiment to 8.53±1.36s. Meanwhile, it was found that the students in the smart device group and the control group produced very significant differences in the indexes of deep squatting, shoulder flexibility, and active straight-leg raising (P<0.01). The intelligent devices and systems designed in this paper have a long-term and significant impact on their physical fitness improvement and also effectively promote the clarity and scientificisation of exercise goals in university physical education courses.