Research on Surfer Action Recognition Based on Support Vector Machine and Hidden Markov Model
In August 2016,the International Olympic Committee officially announced that surfing has become an official event of the Tokyo Olympics,which has brought great opportunities for the development of surfing.As an important application of artificial intelligence technology to empower sports,motion recognition records the athlete's movement process by tracking key points in the time domain,and converts it into a mathematical way to express the movement process,which is of great significance for competitive training and national fitness.The re-search uses inertial measurement unit(IMU)to collect data samples,and compares the results of surfer action recognition by using support vector machine(SVM)and hidden Markov(HMM)machine learning methods.The results show that both SVM model(accuracy 83.4%)and HMM model(accuracy 91.4%)can effectively recognize surfer actions,but compared with SVM model,HMM model has higher classification accuracy.This al-gorithm realizes the time-varying motion classification of IMU input data worn during surfing,and provides a new method and new application for the action recognition of non-traditional sports events associated with synchronized videos.
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