SVM-based Support Vector Type Recognition Machine for Smart Things in Soccer Training Motion Recognition

Abstract

With the development of IoT technology, machine learning, and other artificial intelligence technologies, there have been many related technologies applied to the sports industry. Soccer, as the world’s number one sport, has a wide range of popularity, a high degree of attention, and a high degree of commercialization. In the traditional soccer training action recognition methods, there is insufficient collection and in-depth profiling of real data, and what is not available is soccer movement action capture and recognition based on kinematic knowledge. To address the above shortcomings, this study designs a Support Vector Machine SVM-based intelligent IoT-type soccer training movement recognition and evaluation framework, and constructs a machine learning algorithmic model to recognize, evaluate and analyze soccer training movements. Common feature extraction methods are suitable for recognizing most monotonous movements, but soccer movements are highly variable and athletes’ ankle movements are flexible and changeable. The actual acquired soccer data streams are noisy and the data patterns are not obvious, and the performance of the model to recognize the data will be degraded. To extract the effective feature values in the complex data stream and improve the correct degree of pattern recognition, the classification pattern of attitude angle type solving + SVM classification algorithm is constructed. The experimental results show that the designed algorithmic pattern based on the posture angular pattern solving + SVM classification algorithm pattern for soccer training movement recognition can reach 90% accuracy in recognizing different movements, which is extremely suitable for the recognition of soccer training movements.