ISB clinical biomechanics award winner 2021: Tibio-femoral kinematics of natural versus replaced knees – A comparison using dynamic videofluoroscopy

Postolka, Barbara; Taylor, William R.; List, Renate; Fucentese, Sandro F.; Koch, Peter P.; Schütz, Pascal

doi:10.1016/j.clinbiomech.2022.105667

Cited by 6 publications

(6 citation statements)

References 55 publications

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“…In clinical movement biomechanics, kinematics can be plotted to characterise joint motion over time. Two (or more) of these signals are then often compared to determine whether significant differences in kinematic patterns are associated with, for example, different pathologies 4 , 5 , disease stages 6 , 7 , treatment strategies 8 , 9 , or measurement systems 10 – 12 . In previous work, inertial-based knee kinematic estimates were compared against ground truth data generated by a calibrated joint simulator, using an analytical approach that allowed flexibility in the orientation of the sensor placement on the joint segments 13 .…”

Section: Introductionmentioning

confidence: 99%

A frame orientation optimisation method for consistent interpretation of kinematic signals

Vásquez

Taylor

Maas

et al. 2023

Sci Rep

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In clinical movement biomechanics, kinematic data are often depicted as waveforms (i.e. signals), characterising the motion of articulating joints. Clinically meaningful interpretations of the underlying joint kinematics, however, require an objective understanding of whether two different kinematic signals actually represent two different underlying physical movement patterns of the joint or not. Previously, the accuracy of IMU-based knee joint angles was assessed using a six-degrees-of-freedom joint simulator guided by fluoroscopy-based signals. Despite implementation of sensor-to-segment corrections, observed errors were clearly indicative of cross-talk, and thus inconsistent reference frame orientations. Here, we address these limitations by exploring how minimisation of dedicated cost functions can harmonise differences in frame orientations, ultimately facilitating consistent interpretation of articulating joint kinematic signals. In this study, we present and investigate a frame orientation optimisation method (FOOM) that aligns reference frames and corrects for cross-talk errors, hence yielding a consistent interpretation of the underlying movement patterns. By executing optimised rotational sequences, thus producing angular corrections around each axis, we enable a reproducible frame definition and hence an approach for reliable comparison of kinematic data. Using this approach, root-mean-square errors between the previously collected (1) IMU-based data using functional joint axes, and (2) simulated fluoroscopy-based data relying on geometrical axes were almost entirely eliminated from an initial range of 0.7°–5.1° to a mere 0.1°–0.8°. Our results confirm that different local segment frames can yield different kinematic patterns, despite following the same rotation convention, and that appropriate alignment of reference frame orientation can successfully enable consistent kinematic interpretation.

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Section: Introductionmentioning

confidence: 99%

A frame orientation optimisation method for consistent interpretation of kinematic signals

Vásquez

Taylor

Maas

et al. 2023

Sci Rep

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“…All volunteers underwent clinical examination, video-fluoroscopic assessment, computer-tomography (CT), and long-legged radiograph of the lower limbs to allow the effect of bony morphology on tibio-femoral kinematics to be analyzed. The sample size was the result after a prospective enrolment for a previous tibio-femoral kinematic analysis [ 18 ]. A thorough clinical knee examination was performed by an experienced knee surgeon in advance to confirm the physiologically healthy range of motion (RoM) and ligament integrity.…”

Section: Methodsmentioning

confidence: 99%

“…One subject did not complete all gait tasks and was excluded, leaving a total of 26 subjects (12 left, 14 right knees) for final analysis. The side included for analysis was chosen to allow a well-balanced distribution of varus/valgus alignment as defined for the previous study cohort [ 18 ]. Demographic characteristics, frontal, and axial leg alignment are summarized in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

“…Motion tasks and tibio-femoral kinematics were analyzed using a moving fluoroscope for a previous study by Postolka et al [ 18 ] and summarized hereafter. Radiographic images were acquired using a moving fluoroscope (25/30 Hz, 1 ms shutter time, image resolution 1000 × 1000 pixels, Figure 2 ) synchronized with a 22-camera optical tracking system [ 21 ] throughout complete cycles of three distinct gait tasks: level walking, downhill walking (10° declined slope), and stair descent (three steps, each 18 cm in height).…”

Section: Methodsmentioning

confidence: 99%

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Influence of Bone Morphology on In Vivo Tibio-Femoral Kinematics in Healthy Knees during Gait Activities

Hodel

Postolka

Flury

et al. 2022

JCM

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An improved understanding of the relationships between bone morphology and in vivo tibio-femoral kinematics potentially enhances functional outcomes in patients with knee disorders. The aim of this study was to quantify the influence of femoral and tibial bony morphology on tibio-femoral kinematics throughout complete gait cycles in healthy subjects. Twenty-six volunteers underwent clinical examination, radiographic assessment, and dynamic video-fluoroscopy during level walking, downhill walking, and stair descent. Femoral computer-tomography (CT) measurements included medial condylar (MC) and lateral condylar (LC) width, MC and LC flexion circle, and lateral femoral condyle index (LFCI). Tibial CT measurements included both medial (MTP) and lateral tibial plateau (LTP) slopes, depths, lengths, and widths. The influence of bony morphology on tibial internal/external rotation and anteroposterior (AP)-translation of the lateral and medial compartments were analyzed in a multiple regression model. An increase in tibial internal/external rotation could be demonstrated with decreasing MC width β: −0.30 (95% CI: −0.58 to −0.03) (p = 0.03) during the loaded stance phase of level walking. An increased lateral AP-translation occurred with both a smaller LC flexion circle β: −0.16 (95% CI: −0.28 to −0.05) (p = 0.007) and a deeper MTP β: 0.90 (95% CI: 0.23 to 1.56) (p = 0.01) during the loaded stance phase of level walking. The identified relationship between in vivo tibio-femoral kinematics and bone morphology supports a customized approach and individual assessment of these factors in patients with knee disorders and potentially enhances functional outcomes in anterior cruciate ligament injuries and total knee arthroplasty.

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“…In the field of orthopaedics, the use of joint kinematic data to objectively quantify patient function and mobility before and after treatment can markedly improve medical outcomes [ 1 , 2 , 3 ]. Current gold standard technologies are often considered to be static [ 4 , 5 ] or dynamic [ 3 , 6 , 7 ] fluoroscopy or in some cases even marker-based optical motion capture systems [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Validation of Inertial-Measurement-Unit-Based Ex Vivo Knee Kinematics during a Loaded Squat before and after Reference-Frame-Orientation Optimisation

Sagasser,

Sauer,

Thorwächter

et al. 2024

Sensors

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Recently, inertial measurement units have been gaining popularity as a potential alternative to optical motion capture systems in the analysis of joint kinematics. In a previous study, the accuracy of knee joint angles calculated from inertial data and an extended Kalman filter and smoother algorithm was tested using ground truth data originating from a joint simulator guided by fluoroscopy-based signals. Although high levels of accuracy were achieved, the experimental setup leveraged multiple iterations of the same movement pattern and an absence of soft tissue artefacts. Here, the algorithm is tested against an optical marker-based system in a more challenging setting, with single iterations of a loaded squat cycle simulated on seven cadaveric specimens on a force-controlled knee rig. Prior to the optimisation of local coordinate systems using the REference FRame Alignment MEthod (REFRAME) to account for the effect of differences in local reference frame orientation, root-mean-square errors between the kinematic signals of the inertial and optical systems were as high as 3.8° ± 3.5° for flexion/extension, 20.4° ± 10.0° for abduction/adduction and 8.6° ± 5.7° for external/internal rotation. After REFRAME implementation, however, average root-mean-square errors decreased to 0.9° ± 0.4° and to 1.5° ± 0.7° for abduction/adduction and for external/internal rotation, respectively, with a slight increase to 4.2° ± 3.6° for flexion/extension. While these results demonstrate promising potential in the approach’s ability to estimate knee joint angles during a single loaded squat cycle, they highlight the limiting effects that a reduced number of iterations and the lack of a reliable consistent reference pose inflicts on the sensor fusion algorithm’s performance. They similarly stress the importance of adapting underlying assumptions and correctly tuning filter parameters to ensure satisfactory performance. More importantly, our findings emphasise the notable impact that properly aligning reference-frame orientations before comparing joint kinematics can have on results and the conclusions derived from them.

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ISB clinical biomechanics award winner 2021: Tibio-femoral kinematics of natural versus replaced knees – A comparison using dynamic videofluoroscopy

Cited by 6 publications

References 55 publications

A frame orientation optimisation method for consistent interpretation of kinematic signals

A frame orientation optimisation method for consistent interpretation of kinematic signals

Influence of Bone Morphology on In Vivo Tibio-Femoral Kinematics in Healthy Knees during Gait Activities

Validation of Inertial-Measurement-Unit-Based Ex Vivo Knee Kinematics during a Loaded Squat before and after Reference-Frame-Orientation Optimisation

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