Magnetic Condition-Independent 3D Joint Angle Estimation Using Inertial Sensors and Kinematic Constraints

Abstract

In biomechanics, joint angle estimation using wearable inertial measurement units (IMUs) has been getting great popularity. However, magnetic disturbance issue is considered problematic as the disturbance can seriously degrade the accuracy of the estimated joint angles. This study proposes a magnetic condition-independent three-dimensional (3D) joint angle estimation method based on IMU signals. The proposed method is implemented in a sequential direction cosine matrix-based orientation Kalman filter (KF), which is composed of an attitude estimation KF followed by a heading estimation KF. In the heading estimation KF, an acceleration-level kinematic constraint from a spherical joint replaces the magnetometer signals for the correction procedure. Because the proposed method does not rely on the magnetometer, it is completely magnetic condition-independent and is not affected by the magnetic disturbance. For the averaged root mean squared errors of the three tests performed using a rigid two-link system, the proposed method produced 1.58 • , while the conventional method with the magnetic disturbance compensation mechanism produced 5.38 • , showing a higher accuracy of the proposed method in the magnetically disturbed conditions. Due to the independence of the proposed method from the magnetic condition, the proposed approach could be reliably applied in various fields that require robust 3D joint angle estimation through IMU signals in an unspecified arbitrary magnetic environment. reference, forming an inertial and magnetic measurement unit (IMMU). Last two decades, much progress has been made in joint angle estimation using IMUs or IMMUs [1,2,5,[7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25].Seel et al.[7] estimated the flexion/extension angles of the knee and ankle during walking based on IMUs because the flexion/extension is the rotation around one dominant axis of the joint. El-Gohary and McNames [8] presented an unscented Kalman filter (UKF) incorporated with a kinematic arm model to track the shoulder and elbow joint angles using IMUs. Later, the UKF in [8] was improved in [5] and [9] by imposing physical constraints on the range of motion for each joint and using zero-velocity updates to mitigate the effect of the sensor drift. In [10], Vikas and Crane presented an approach to estimate the revolute and universal joint angles independent of the integration errors/drift using strategically placed accelerometers and gyroscopes. In addition, joint angle estimation using IMU signals was discussed in [13][14][15] and [20]. However, all of these studies dealt with one-dimensional or two-dimensional joint angles using IMUs, instead of 3D joint angles using IMMUs.While magnetometers in IMMUs are required to obtain the 3D joint angles, utilization of magnetometers brings a well-known magnetic disturbance issue in angle estimation [26][27][28][29]. The magnetometer can provide a constant heading reference vector, which is the geomagnetic field vector surrounding the sensor, only when the ...

A novel IMU-based clinical assessment protocol for Axial Spondyloarthritis: a protocol validation study

“…It should be noted that the slow spine movements in this study will rely more on the stabilisation of the accelerometers, whereas the high pace but less broad movements of walking and running rely more on the quick response of the gyroscopes. A recent study by Lee & Jeon (2019) obtained very promising results by excluding the magnetometer data and applying kinematic constraints, reaching a RMSE of 1.58 . These results are not fully comparable with the present study, as the Kalman filter used in the presented was not customisable and raw data were inaccessible to the authors.…”

Peer Review #2 of "A novel IMU-based clinical assessment protocol for Axial Spondyloarthritis: a protocol validation study (v0.1)"

“…It should be noted that the slow spine movements in this study will rely more on the stabilization of the accelerometers, whereas the high pace but less broad movements of walking and running rely more on the quick response of the gyroscopes. A recent study by Lee & Jeon (2019) obtained very promising results by excluding the magnetometer data and applying kinematic constraints, reaching a RMSE of 1.58°. These results are not fully comparable with the present study, as the Kalman filter used in the presented was not customisable and raw data were inaccessible to the authors.…”

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“…The human body has constrained degree of freedom and temporal coherence and smoothness is an important feature of human motion. Many existing kinematic or inverse kinematic based i-Mocap frameworks, therefore uses predefined constraints to reduce measurement errors or drifts [2][3][4][5][6]. In past research [7][8][9], a small set of inertial sensors is shown to estimate 3D pose to a reasonable accuracy.…”