基于多通道注意力的人体跳跃屈膝角度识别模型OA

Currently,the combination of deep learning and wearable devices is widely used in the field of quantitative movement assessment and shows important theoretical value and application potential.However,traditional neural network methods are difficult to effectively extract key multidimensional features when dealing with fine-grained actions with different motion qualities,which limits their performance in accu-rately recognizing and deeply analyzing individual motion behaviors.To address this problem,a multi-channel attention model(Conv+CEA+AG)is proposed,aiming to enhance the ability to capture important features of the action through the multi-channel attention(CEA)mecha-nism,and thus improve the accuracy of the analysis of motor behavior.Firstly,human jumping motion data under different knee flexion angles are collected,and the sensor signals are preprocessed to extract effective jumping cycle signals in order to construct a jumping motion dataset.Subsequently,a neural network with integrated multi-channel attention is applied to the dataset to construct a jumping motion knee-bending angle detection model.The experimental results show that the proposed model achieves 94.32%accuracy in the classification task and 2.04° mean absolute error in the regression task.Compared with the traditional neural network model(Conv+GRU)and the previous proposed model(Conv+CIE+AG),the accuracy is improved by 12.47%and 6.08%,and the average absolute error decrease by 3.49° and 3.09°,respective-ly,which prove that the model demonstrate a better performance in knee-bending angle recognition.