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.

This paper describes the use of nine-axis motion sensors to evaluate knee joint angle estimation accuracy during walking. The nine-axis motion sensor comprises a three-axis gyro sensor, a three-axis acceleration sensor and a three-axis geomagnetic sensor. It can estimate joint angles during exercise by correcting the drift of the three-axis gyro sensor using information obtained from the other two sensors. Human movement results from the rotational motion of the respective joints, so that the proportion of the centrifugal acceleration and the tangential acceleration in the output of the acceleration sensor increases during exercise. Processing the centrifugal acceleration and tangential acceleration appropriately and ascertaining the degree of estimation error are important for improving the joint angle estimation accuracy. For this study, the authors produced a sensor fusion algorithm using an extended Kalman filter to correct the acceleration sensor output. The sensor fusion algorithm uses information obtained from the nine-axis motion sensors to estimate the knee joint angle by correcting the centrifugal acceleration and tangential acceleration. During the experiment, the 3D motion analysis system and two nine-axis motion sensors measured walking exercise. The knee joint angle was estimated using an extended Kalman filter with information obtained from the nine-axis motion sensors. We evaluated the system accuracy for knee joint angle estimation by comparing the nine-axis motion sensor results and the 3D motion analysis system results. This analytical method is anticipated for use in estimating motion in sports and healthcare applications.

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To consider the experimental results presented in the previous paper, theoretical study has been made on the accumulator behavior of an oil hydraulic breaker. The theoretical results which are compared with the experimental results are as follows: (1) With respect to the effect of the accumulator gas pressure and volume, the theoretical results agree well with the experimental results except for the conditions that the pressure and volume are small. The optimal values are indicated as that the gas pressure is about 3.43 MPa and the volume is about 0.2 to 0.3l. (2) The expression to estimate the accumulator gas volume is obtained, and it is a function of a pressure drop ratio of the rear chamber at the forward stroke of piston and effective discharge volume of the accumulator. Various oil hydraulic breakers are given optimal sizes of the accumulators by using the expression.