This paper presents an extended Kalman filter for pose estimation using noise covariance matrices based on sensor output. Compact and lightweight nine-axis motion sensors are used for motion analysis in widely various fields such as medical welfare and sports. A nine-axis motion sensor includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. Information obtained from the three sensors is useful for estimating joint angles using the Kalman filter. The extended Kalman filter is used widely for state estimation because it can estimate the status with a small computational load. However, determining the process and observation noise covariance matrices in the extended Kalman filter is complicated. The noise covariance matrices in the extended Kalman filter were found for this study based on the sensor output. Postural change appears in the gyroscope output because the rotational motion of the joints produces human movement. Therefore, the process noise covariance matrix was determined based on the gyroscope output. An observation noise covariance matrix was determined based on the accelerometer and magnetometer output because the two sensors’ outputs were used as observation values. During a laboratory experiment, the lower limb joint angles of three participants were measured using an optical 3D motion analysis system and nine-axis motion sensors while participants were walking. The lower limb joint angles estimated using the extended Kalman filter with noise covariance matrices based on sensor output were generally consistent with results obtained from the optical 3D motion analysis system. Furthermore, the lower limb joint angles were measured using nine-axis motion sensors while participants were running in place for about 100 s. The experiment results demonstrated the effectiveness of the proposed method for human pose estimation.

中文翻译：

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基于传感器输出，使用噪声协方差矩阵通过扩展卡尔曼滤波器进行姿态估计

本文提出了一种扩展的卡尔曼滤波器，用于基于传感器输出的噪声协方差矩阵进行姿态估计。紧凑轻巧的九轴运动传感器用于医疗福利和体育等广泛领域的运动分析。九轴运动传感器包括三轴陀螺仪，三轴加速度计和三轴磁力计。从三个传感器获得的信息对于使用卡尔曼滤波器估计关节角度非常有用。扩展卡尔曼滤波器被广泛用于状态估计，因为它可以用很小的计算量来估计状态。但是，在扩展卡尔曼滤波器中确定过程和观察噪声协方差矩阵很复杂。基于传感器的输出，本研究发现了扩展卡尔曼滤波器中的噪声协方差矩阵。姿势变化出现在陀螺仪输出中，因为关节的旋转运动会引起人体运动。因此，基于陀螺仪的输出确定了过程噪声协方差矩阵。由于将两个传感器的输出用作观测值，因此基于加速度计和磁力计的输出确定了观测噪声协方差矩阵。在实验室实验期间，当参与者步行时，使用光学3D运动分析系统和九轴运动传感器测量了三名参与者的下肢关节角度。使用基于传感器输出的带有噪声协方差矩阵的扩展卡尔曼滤波器估计的下肢关节角度通常与从光学3D运动分析系统获得的结果一致。此外，参与者在原地奔跑约100 s时，使用九轴运动传感器测量下肢关节角度。实验结果证明了该方法在人体姿态估计中的有效性。