Validity of inertial sensor based 3D joint kinematics of static and dynamic sport and physiotherapy specific movements

Abstract

3D joint kinematics can provide important information about the quality of movements. Optical motion capture systems (OMC) are considered the gold standard in motion analysis. However, in recent years, inertial measurement units (IMU) have become a promising alternative. The aim of this study was to validate IMU-based 3D joint kinematics of the lower extremities during different movements. Twenty-eight healthy subjects participated in this study. They performed bilateral squats (SQ), single-leg squats (SLS) and countermovement jumps (CMJ). The IMU kinematics was calculated using a recently-described sensor-fusion algorithm. A marker based OMC system served as a reference. Only the technical error based on algorithm performance was considered, incorporating OMC data for the calibration, initialization, and a biomechanical model. To evaluate the validity of IMU-based 3D joint kinematics, root mean squared error (RMSE), range of motion error (ROME), Bland-Altman (BA) analysis as well as the coefficient of multiple correlation (CMC) were calculated. The evaluation was twofold. First, the IMU data was compared to OMC data based on marker clusters; and, second based on skin markers attached to anatomical landmarks. The first evaluation revealed means for RMSE and ROME for all joints and tasks below 3°. The more dynamic task, CMJ, revealed error measures approximately 1° higher than the remaining tasks. Mean CMC values ranged from 0.77 to 1 over all joint angles and all tasks. The second evaluation showed an increase in the RMSE of 2.28°– 2.58° on average for all joints and tasks. Hip flexion revealed the highest average RMSE in all tasks (4.87°– 8.27°). The present study revealed a valid IMU-based approach for the measurement of 3D joint kinematics in functional movements of varying demands. The high validity of the results encourages further development and the extension of the present approach into clinical settings.

Lower Limb Kinematics Using Inertial Sensors during Locomotion: Accuracy and Reproducibility of Joint Angle Calculations with Different Sensor-to-Segment Calibrations

“…The accuracy reported in this study during straight walking ranged from 1.1 • to 3.6 • for RMSE and from 0.2 • to 3.4 • for ∆ROM, which is comparable to the mean error below 3 • reported when using marker clusters on segments [22] rather than markers on anatomical landmarks [23][24][25]. Indeed, both methods reported different joint kinematics and accounted differently for errors of markers placement, soft tissue artefacts, and biomechanical model calculations [22]. The functional calibration of the optical system did not influence the accuracy, indicating that the reference frame obtained for each segment with the functional calibration movements were close to the optical reference frame.…”

Validation of a Device to Measure Knee Joint Angles for a Dynamic Movement

“…However, there are several drawbacks to IMU usage such as artifact motion and consistent placement of the devices. Several studies have validated the accuracy of IMUs to measure knee angles in tasks such as gait analyses and sit-to-stand, and have found moderate to strong correlations between their selected IMUs and a motion camera system [27][28][29][30][31]. Because IMUs have been shown to be accurate in these settings, we wanted to determine whether they could be used to measure knee angles during more dynamic motions.…”

Discriminant validity of 3D joint kinematics and centre of mass displacement measured by inertial sensor technology during the unipodal stance task

“…Nevertheless, completely avoiding the ferromagnetic interference, when measuring inside a laboratory is not possible. New methods have been developed that overcome these constraints by tracking motion without using the magnetometer [37,38]. However, these methods need further development before they can be implemented in clinical research.…”