摘要
近年来,惯性传感器在人体动作识别中的应用受到了广泛关注,但用户在重新穿戴惯性传感器时,不能保证每次的固定位置完全一致,这会影响识别精度.针对此问题,本文提出一种利用旋转矩阵实现惯性传感器位置校正的人体动作识别方法.首先,将惯性传感器按照不同的位置固定在手腕处采集动作数据.然后,根据矩阵旋转变换原理,通过标准固定位置与其他固定位置的基准数据求取旋转矩阵.最后,对动作数据提取时频域特征,并构造加权BP神经网络模型以验证校正方法的有效性.同时还讨论了不同的数据融合方法对动作识别的影响.结果表明,校正后的动作数据的识别准确率分别为84.25%,85.94%,相比校正前提高了66.16%,54.35%,说明该方法是有效的.
In recent years,the application of inertial sensors in human action recognition has received extensive attention,but users cannot guarantee that the fixed positions are completely consistent each time they wear inertial sensors again,which can affect the recognition accuracy.Aiming at this problem,this paper proposes a human action recognition method using rotation matrix to realize the position correction of inertial sensors.Firstly,the inertial sensors are fixed at the wrists according to different positions to collect action data.Secondly,the rotation matrices are obtained from benchmark data of the standard fixed position and other fixed positions according to the principle of matrix rotation transformation.Finally,the time-frequency domain features of the action data are extracted,and the weighted back propagation(BP)neural network model is constructed to verify the effectiveness of the correction method.It also discusses the influence of different data fusion methods on action recognition.As a result,the action accuracies of the corrected test data are 84.25%,85.94%,which are increased by 66.16%,54.35%compared with those before correction.It shows that the method is effective.
作者
赵琰
郭明
孙建强
邱建龙
ZHAO Yan;GUO Ming;SUN Jian-qiang;QIU Jian-long(School of Automation and Electrical Engineering,Linyi University,Linyi Shandong 276005,China)
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2021年第11期1883-1890,共8页
Control Theory & Applications
基金
国家自然科学基金项目(61903170,11805091,61877033,61833005)
山东省高等学校青年创新团队发展计划项目资助。
关键词
传感器系统
位置校正
旋转矩阵
动作识别
加权BP神经网络
sensor system
position correction
rotation matrix
action recognition
weighted BP neural network
