Inertial measurement unit (IMU)-based 3D joint angle estimation have a wide range of important applications, among them, in gait analysis and exoskeleton robot control. Conventionally, the joint angle was determined via the estimation of 3D orientation of each body segment using 9-axis IMUs including 3-axis magnetometers. However, a magnetometer is limited by magnetic disturbance in the vicinity of the sensor, which highly affects the accuracy of the joint angle. Accordingly, this study aims to estimate the joint angle using the 6-axis IMU signals composed of a 3-axis accelerometer and a 3-axis gyroscope without a magnetometer. This paper proposes a recurrent neural network (RNN) model, which indirectly utilizes the joint kinematic constraint and thus estimates joint angles based on 6-axis IMUs without using a magnetometer signal. The performance of the proposed model was validated for a mechanical joint and human elbow joint, under magnetically disturbed environments. Experimental results showed that the proposed RNN approach outperformed the conventional approach based on a Kalman filter (KF), i.e., RNN 3.48° vs. KF 10.01° for the mechanical joint and RNN 7.39° vs. KF 21.27° for the elbow joint.

Three-axis magnetometers are widely used in various fields requiring azimuth information. However, accuracy of azimuth estimation based on magnetometer signals may be degraded because of errors such as offset, scale factor, nonorthogonality, hard-iron distortion, and soft-iron distortion. Recently, several ellipsoid-fitting calibration techniques have been proposed and have received much attention. However, comparative analysis of calibration accuracies between these techniques has not been conducted. This study compared and analyzed performance of four ellipsoid-fitting magnetometer calibration techniques such as the linear least square method, the two-step algorithm, and two different nonlinear least square methods. Our analysis and experimental results reveal superiority of the linear least square method compared to other methods in terms of calibration accuracy as well as ease of use in practice.