Standardized assessment of rock-climbing movements based on posture recognition

Aiming at the improper rock climbing movements may cause sports injuries, and the traditional sports motion capture analysis method is insufficient in real time, the accuracy of movement evaluation is low and other problems, this paper applies the pose recognition technology to rock climbing, and researches on the recognition and standard degree evaluation of rock climbing movements. Firstly, a rock climbing action detection algorithm based on improved YOLOv8-Pose is proposed, MobileNetV3 is used as the backbone feature extraction network of the model to realize the lightweight of the model, and the attention mechanism CBAM module is introduced to pay attention to the important channel information in the network, so that more effective feature information can be extracted from the channel and spatial dimensions, and the model recognition accuracy can be improved; then, for the the characteristics of rock climbing action, eight joint angle indicators are selected, and the degree of standard rock climbing action is evaluated using action similarity. Experiments show that: the size of the improved detection model in this paper is reduced by 44.07%compared with the original model, and the number of parameters is also reduced by 51.54%, and the results have a better differentiation when comparing the standard action and the action to be tested by using the action similarity, thus verifying the effectiveness of the algorithm. Thus, the validity of the algorithm is verified.