软件导刊2026，Vol.25Issue(4)：48-56,9.DOI:10.11907/rjdk.251093
基于多通道注意力的人体跳跃屈膝角度识别模型
Human Jumping Knee-Bending Angle Recognition Model Based on Multi-Channel Attention
摘要
Abstract
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.关键词
人体跳跃/屈膝角度识别/可穿戴传感器/深度学习/多通道注意力Key words
human jumping/knee-bending angle recognition/wearable sensor/deep learning/multi-channel attention分类
信息技术与安全科学引用本文复制引用
丁磊,方晨安,胡新荣,李阳,明文凯,林林,吴渊..基于多通道注意力的人体跳跃屈膝角度识别模型[J].软件导刊,2026,25(4):48-56,9.基金项目
湖北省自然科学基金一般项目(2022CFB563) （2022CFB563）
