Action recognition for sports combined training based on wearable sensor technology and SVM prediction

In the field of sports, coaches have mainly relied on observing the performance of athletes on the spot to formulate suitable training plans for athletes, which has extremely high requirements for the professionalism of coaches. Based on the above requirements, this paper designs a sports action recognition system for sports enthusiasts based on the SVM algorithm optimization model, and for the purpose of verifying the applicability of the system to different sports fields, experiments are carried out on basketball actions and race walking actions. The system uses wearable sensors to capture the motion data of the user, and then analyzes and identifies the user's actions through the SVM algorithm optimization model. By standardizing the user's sports combination training under the system algorithm, the user can improve their training efficiency and reduce the risk of injury. To establish the human body motion model, this paper divides the human skeleton model into five motion branches. The rotation freedom constraints and joint rotation angle range limits are added to the model to ensure the accuracy of the motion analysis. Combining the forward kinematics of the robot and the homogeneous coordinate transformation, the human body joint rotation motion model and the human bone position and posture model are established. In the end, the user can standardize the sports combination training under the system algorithm. In this paper, through the research of wearable sensor technology and sports combined training action recognition, and apply it to practical life, it aims to promote its development and application.