Liu Kun, Inoue Yoshio, Shibata Kyoko
Department of Intelligent Mechanical Systems Engineering, Kochi University of Technology, 185 Miyanokuchi, Tosayamada-cho, Kochi 782-8502, Japan.
Comput Methods Biomech Biomed Engin. 2012;15(2):203-10. doi: 10.1080/10255842.2010.522184. Epub 2011 Jun 24.
An approach using a physical sensor difference-based algorithm and a virtual sensor difference-based algorithm to visually and quantitatively confirm lower limb posture was proposed. Three accelerometers and two MAG(3)s (inertial sensor module) were used to measure the accelerations and magnetic field data for the calculation of flexion/extension (FE) and abduction/adduction (AA) angles of hip joint and FE, AA and internal/external rotation (IE) angles of knee joint; then, the trajectories of knee and ankle joints were obtained with the joint angles and segment lengths. There was no integration of acceleration or angular velocity for the joint rotations and positions, which is an improvement on the previous method in recent literature. Compared with the camera motion capture system, the correlation coefficients in five trials were above 0.91 and 0.92 for the hip FE and AA, respectively, and higher than 0.94, 0.93 and 0.93 for the knee joint FE, AA and IE, respectively.
提出了一种使用基于物理传感器差异的算法和基于虚拟传感器差异的算法来直观且定量地确定下肢姿势的方法。使用三个加速度计和两个MAG(3)（惯性传感器模块）来测量加速度和磁场数据，以计算髋关节的屈伸（FE）和外展/内收（AA）角度以及膝关节的FE、AA和内/外旋转（IE）角度；然后，利用关节角度和节段长度获得膝关节和踝关节的轨迹。对于关节旋转和位置，没有对加速度或角速度进行积分，这是对近期文献中先前方法的一种改进。与摄像机运动捕捉系统相比，在五次试验中，髋关节FE和AA的相关系数分别高于0.91和0.92，膝关节FE、AA和IE的相关系数分别高于0.94、0.93和0.93。
