Soft tissue artifact compensation in knee kinematics by multi-body optimization: Performance of subject-specific knee joint models

Clément, Julien; Dumas, Raphaël; Hagemeister, Nicola; Guise, Jacques A. de

doi:10.1016/j.jbiomech.2015.09.040

Cited by 62 publications

(57 citation statements)

References 32 publications

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“…In this state, the accelerometer measurements combined with the presented measurement model (4) do not yield information on the relative heading between sensors. Although the proposed method relies on the presence of acceleration, low dynamic activities such as gait proved to contain sufficient acceleration to correct for drift in the joint angle in Section V. A limitation of the current study might be the occurrence of shocks and vibrations which causes some special caution in sports applications [25]. The position of the sensor with respect to the joint center is assumed to be fixed, which does not allow for soft tissue artifacts.…”

Section: Discussionmentioning

confidence: 96%

Drift-Free Inertial Sensor-Based Joint Kinematics for Long-Term Arbitrary Movements

et al. 2020

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The ability to capture joint kinematics in outside-laboratory environments is clinically relevant. In order to estimate kinematics, inertial measurement units can be attached to body segments and their absolute orientations can be estimated. However, the heading part of such orientation estimates is known to drift over time, resulting in drifting joint kinematics. This study proposes a novel joint kinematic estimation method that tightly incorporates the connection between adjacent segments within a sensor fusion algorithm, to obtain drift-free joint kinematics. Drift in the joint kinematics is eliminated solely by utilizing common information in the accelerometer and gyroscope measurements of sensors placed on connecting segments. Both an optimization-based smoothing and a filtering approach were implemented. Validity was assessed on a robotic manipulator under varying measurement durations and movement excitations. Standard deviations of the estimated relative sensor orientations were below 0.89 • in an optimization-based smoothing implementation for all robot trials. The filtering implementation yielded similar results after convergence. The method is proven to be applicable in biomechanics, with a prolonged gait trial of 7 minutes on 11 healthy subjects. Three-dimensional knee joint angles were estimated, with mean RMS errors of 2.14 • , 1.85 • , 3.66 • in an optimization-based smoothing implementation and mean RMS errors of 3.08 • , 2.42 • , 4.47 • in a filtering implementation, with respect to a golden standard optical motion capture reference system. Tommy Verbeerst received the M.Sc. degree in electrical engineering from KHBO, Ostend, Belgium, in 2008. Since 2013, he has been working with KU Leuven and UC Vives. He is affiliated with the Department of Electrical Engineering (ESAT), KU Leuven Campus Bruges, Belgium. His current research interest includes the fields of engineering education, robotics, and machine-vision. Mark Versteyhe received the M.Sc. degree in mechanical engineering and the Ph.D. degree in applied sciences, from KU Leuven in 1995 and 2000, respetively.He has worked 16 years in industry in various functions linked to research and innovation. Since October 2016, he has been a Professor with KU Leuven's Faculty of Engineering Technology, Technology Campus Brugge, where he co-ordinates the research effort on connected mechatronics. His research focus lies in studying and applying the holistic paradigm of mechatronic system design. His special interest goes to "Dependability" which encompasses reliabilityavailability-robustness and security of a system and "Distributed Systems" which are treated as a complex ecosystem of machines and humans that are connected within the Industry 4.0 paradigm shift.Kurt Claeys received the M.Sc. degree in musculoskeletal rehabilitation sciences and physiotherapy from the University of Ghent, Belgium, in 1993, and the Ph.D. degree in orthopedic manual therapist from the IRSK-WINGS institute Ieper, Belgium, in 2005, and the Ph.D. degree from KU Leuven, Belgium,...

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Section: Discussionmentioning

confidence: 96%

Drift-Free Inertial Sensor-Based Joint Kinematics for Long-Term Arbitrary Movements

et al. 2020

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“…In that case the procedure, instead of compensating for STA as originally intended, adds modelling errors to it. Subject-specific joint models embedded in MBO, in particular models Spherical and Parallel (Clément et al, 2015), have been shown to have a beneficial impact on kinematics estimation.…”

Section: Limitationsmentioning

confidence: 99%

Comparative assessment of knee joint models used in multi-body kinematics optimisation for soft tissue artefact compensation

Richard

Cappozzo

Dumas

2017

Journal of Biomechanics

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Estimating joint kinematics from skin-marker trajectories recorded using stereophotogrammetry is complicated by soft tissue artefact (STA), an inexorable source of error. One solution is to use a bone pose estimator based on multi-body kinematics optimisation (MKO) embedding joint constraints to compensate for STA. However, there is some debate over the effectiveness of this method. The present study aimed to quantitatively assess the degree of agreement between reference (i.e., artefact-free) knee joint kinematics and the same kinematics estimated using MKO embedding six different knee joint models. The following motor tasks were assessed: level walking, hopping, cutting, running, sit-to-stand, and step-up. Reference knee kinematics was taken from pin-marker or biplane fluoroscopic data acquired concurrently with skin-marker data, made available by the respective authors. For each motor task, Bland-Altman analysis revealed that the performance of MKO varied according to the joint model used, with a wide discrepancy in results across degrees of freedom (DoFs), models and motor tasks (with a bias between -10.2° and 13.2° and between -10.2mm and 7.2mm, and with a confidence interval up to ±14.8° and ±11.1mm, for rotation and displacement, respectively). It can be concluded that, while MKO might occasionally improve kinematics estimation, as implemented to date it does not represent a reliable solution to the STA issue.

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“…Optoelectronic measures always include soft-tissue artefacts that will ultimately influence the markers movements and therefore the inverse kinematics computations [40]. Various methods have been proposed including global optimization, rigid marker clusters, or even bone pins [41]. This research however makes use of a marker set based on bony landmarks and a healthy population without extreme body shapes (over-weight or extremely developed muscle mass) and therefore we consider the effect of the wobbling masses on the dynamics as negligible.…”

Section: Discussionmentioning

confidence: 99%

Center of pressure based segment inertial parameters validation

et al. 2017

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By proposing efficient methods for estimating Body Segment Inertial Parameters’ (BSIP) estimation and validating them with a force plate, it is possible to improve the inverse dynamic computations that are necessary in multiple research areas. Until today a variety of studies have been conducted to improve BSIP estimation but to our knowledge a real validation has never been completely successful. In this paper, we propose a validation method using both kinematic and kinetic parameters (contact forces) gathered from optical motion capture system and a force plate respectively. To compare BSIPs, we used the measured contact forces (Force plate) as the ground truth, and reconstructed the displacements of the Center of Pressure (COP) using inverse dynamics from two different estimation techniques. Only minor differences were seen when comparing the estimated segment masses. Their influence on the COP computation however is large and the results show very distinguishable patterns of the COP movements. Improving BSIP techniques is crucial and deviation from the estimations can actually result in large errors. This method could be used as a tool to validate BSIP estimation techniques. An advantage of this approach is that it facilitates the comparison between BSIP estimation methods and more specifically it shows the accuracy of those parameters.

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Soft tissue artifact compensation in knee kinematics by multi-body optimization: Performance of subject-specific knee joint models

Cited by 62 publications

References 32 publications

Drift-Free Inertial Sensor-Based Joint Kinematics for Long-Term Arbitrary Movements

Drift-Free Inertial Sensor-Based Joint Kinematics for Long-Term Arbitrary Movements

Comparative assessment of knee joint models used in multi-body kinematics optimisation for soft tissue artefact compensation

Center of pressure based segment inertial parameters validation

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