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.
In the case of dynamic sports activities such as skiing and sprints, it is difficult to apply optical motion capture systems because of measurement volume limitation. Alternatively, the use of inertial measurement unit (IMU) as a motion sensor has gained attention. This paper proposes a drift reduction method in the IMU-based joint angle estimation for dynamic motion-involved sports applications. To resolve the problem of conventional IMU-based methods significantly reducing performance under highly dynamic conditions, the proposed method applies a correction method using joint constraint. The proposed method is the complementary filter based on the previous drift reduction technique using the joint constraint, but performs in real time. The proposed method was validated by comparing the estimation accuracy with conventional methods under various dynamic conditions. The results showed that the proposed method was superior to the methods that did not use the constraint. While the proposed method was 0.19° less accurate than the non-realtime method of the reference, it is more practical due to its realtime correction capability.
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Previous studies on joint angle estimation have been restricted to slow-speed level walking conditions, even though slope walking and running elicit unique biomechanical characteristics. Measurements were mostly based on an optical motion capture system despite in-the-lab limitation of measurement technique. The contribution of this study is twofold: (i) to propose a joint angle estimation method by applying a state-of-the-art parallel Kalman filter based on an inertial measurement unit (IMU) that can overcome in-the-lab limitation, and (ii) to demonstrate its application to level walking condition as well as slope walking and running conditions to fill a gap in joint kinematics literature. In particular, this study focuses on knee flexion/extension and ankle dorsiflexion/plantarflexion angles at various speed variations. The parallel Kalman filter applied in the proposed method can compensate external acceleration through Markov-chain-based acceleration modeling, that may enhance joint estimation performance in high speed walking conditions. To validate the proposed estimation method, an optical motion capture system was used as reference. In addition, patterns for each condition were investigated to identify and evaluate presence of classifying features.
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