RESUMO
Introduction: Inertial Measurement Units (IMU) require a sensor-to-segment calibration procedure in order to compute anatomically accurate joint angles and, thereby, be employed in healthcare and rehabilitation. Research literature proposes several algorithms to address this issue. However, determining an optimal calibration procedure is challenging due to the large number of variables that affect elbow joint angle accuracy, including 3D joint axis, movement performed, complex anatomy, and notable skin artefacts. Therefore, this paper aims to compare three types of calibration techniques against an optical motion capture reference system during several movement tasks to provide recommendations on the most suitable calibration for the elbow joint. Methods: Thirteen healthy subjects were instrumented with IMU sensors and optical marker clusters. Each participant performed a series of static poses and movements to calibrate the instruments and, subsequently, performed single-plane and multi-joint tasks. The metrics used to evaluate joint angle accuracy are Range of Motion (ROM) error, Root Mean Squared Error (RMSE), and offset. We performed a three-way RM ANOVA to evaluate the effect of joint axis and movement task on three calibration techniques: N-Pose (NP), Functional Calibration (FC) and Manual Alignment (MA). Results: Despite small effect sizes in ROM Error, NP displayed the least precision among calibrations due to interquartile ranges as large as 24.6°. RMSE showed significant differences among calibrations and a large effect size where MA performed best (RMSE = 6.3°) and was comparable with FC (RMSE = 7.2°). Offset showed a large effect size in the calibration*axes interaction where FC and MA performed similarly. Conclusion: Therefore, we recommend MA as the preferred calibration method for the elbow joint due to its simplicity and ease of use. Alternatively, FC can be a valid option when the wearer is unable to hold a predetermined posture.
RESUMO
Electromagnetic (EM) tracking has been used to quantify biomechanical parameters of the lower limb and lumbar spine during ergometer rowing to improve performance and reduce injury. Optical motion capture (OMC) is potentially better suited to measure comprehensive whole-body dynamics in rowing. This study compared accuracy and precision of EM and OMC displacements by simultaneously recording kinematics during rowing trials at low, middle, and high rates on an instrumented ergometer (n=12). Trajectories calculated from OMC and EM sensors attached to the pelvis, lumbar spine, and right leg were highly correlated, but EM tracking lagged behind ergometer and OMC tracking by approximately 6%, yielding large RMS errors. When this phase-lag was corrected by least squares minimization, agreement between systems improved. Both systems demonstrated an ability to adequately track large dynamic compound movements in the sagittal plane but struggled at times to precisely track small displacements and narrow angular ranges in medial/lateral and superior/inferior directions. An OMC based tracking methodology can obtain equivalence with a previously validated EM system, for spine and lower limb metrics. Improvements in speed and consistency of data acquisition with OMC are beneficial for dynamic motion studies. Compatibility ensures continuity by maintaining the ability to compare to prior work.
