Yoga training injury detection method based on multi-sensor information fusion

Abstract

Yoga, as a kind of body building exercise, has always been loved by people. However, many people suffer from yoga training injuries due to long-term incorrect posture and wrong exercise methods. There is an urgent need for a technology to help people detect and improve yoga training methods. Based on the past computer assistance method, this paper started from a new idea, and adopted the method of multi-sensor information fusion to detect yoga training, aiming to help the masses better participate in yoga training. In this study, 50 volunteers were invited to participate in the comparative experiment. Based on multi-sensor information fusion, and by building a human model, the tension and compression data before and after yoga training were compared to analyze the differences before and after calculation. It was concluded that the more sensors, the higher the degree of information fusion, and the lower the yoga training injury index. The injury index of yoga training without multi-sensor information fusion technology in the early stage was 0.39. With the increase of the number of sensors, the injury index of yoga training has gradually decreased to 0.02, which was more than 5 percentage points lower than that of the previous methods. The experiment showed that the method of yoga training damage detection based on multi-sensor information fusion was feasible, which also provided a new idea for the research of yoga training injury detection methods.