Multibody Kinematics Optimization Research Articles

Does IMU redundancy improve multi-body optimization results to obtain lower-body kinematics? A preliminary study says no

Inertial Measurement Units (IMUs) have been proposed as an ecological alternative to optoelectronic systems for obtaining human body joint kinematics. Tremendous work has been done to reduce differences between kinematics obtained with IMUs and optoelectronic systems, by improving sensor-to-segment calibration, fusion algorithms, and by using Multibody Kinematics Optimization (MKO). However, these improvements seem to reach a barrier, particularly on transverse and frontal planes. Inspired by marker-based MKO approach performed via OpenSim, this study proposes to test whether IMU redundancy with MKO could improve lower-limb kinematics obtained from IMUs. For this study, five subjects were equipped with 11 IMUs and 30 reflective markers tracked by 18 optoelectronic cameras. They then performed gait, cycling, and running actions. Four different lower-limb kinematics were computed: one kinematics based on markers after MKO, one kinematics based on IMUs without MKO, and two based on IMUs after MKO performed with OpenSense (one with, and one without, sensor redundancy). Kinematics were compared via Root Mean Square Difference and correlation coefficients to kinematics based on markers after MKO. Results showed that redundancy does not reduce differences with the kinematics based on markers after MKO on frontal and transverse planes comparatively to classic IMU MKO. Sensor redundancy does not seem to impact lower-limb kinematics on frontal and transverse planes, due to the likelihood of the “rigid component” of soft-tissue artefact impacting all sensors located on one segment.

Comparison between passive knee kinematics during surgery and active knee kinematics during walking: A preliminary study.

Recovery of function is among a patient's main expectations when undergoing total knee arthroplasty (TKA). However, normal gait knee function is not always completely restored, which can affect patient satisfaction and quality of life. Computer-assisted surgery (CAS) allows surgeons to evaluate passive knee kinematics intra-operatively. Understanding associations between knee kinematics measured during surgery and during daily activities, such as walking, could help define criteria for success based on knee function and not only on the correct alignment of the implant or the leg. This preliminary study compared passive knee kinematics measured during surgery with active kinematics measured during walking. Eight patients underwent a treadmill gait analysis using the KneeKG™ system both before surgery and three months afterwards. Knee kinematics were measured during CAS both before and after TKA implantation. The anatomical axes of the KneeKG™ and CAS systems were homogenised using a two-level, multi-body kinematics optimisation with a kinematic chain based on the calibration measured during CAS. A Bland-Altman analysis was performed before and after TKA for adduction-abduction angle, internal-external rotation, and anterior-posterior displacement over the whole gait cycle, at the single stance phase and at the swing phase. Homogenising the anatomical axes between CAS and treadmill gait led to limited median bias and limits of agreement (post-surgery -0.6 ± 3.6 deg, -2.7 ± 3.6 deg, and -0.2 ± 2.4 mm for adduction-abduction, internal-external rotation and anterior-posterior displacement, respectively). At the individual level, correlations between the two systems were mostly weak (R2 < 0.3) over the whole gait cycle, indicating low kinematic consistency between the two measurements. However, correlations were better at the phase level, especially the swing phase. The multiple sources of differences did not enable us to conclude whether they came from anatomical and biomechanical differences or from measurement system errors.

Kinetics influence of multibody kinematics optimisation for soft tissue artefact compensation

Soft tissue artefact (STA) remains a major source of error in human movement analysis. The multibody kinematics optimisation (MKO) approach is widely stated as a solution to reduce the effects of STA. This study aimed at assessing the influence of the MKO STA-compensation on the errors of estimation of the knee intersegment moments. Experimental data were issued from the CAMS-Knee dataset where six participants with instrumented total knee arthroplasty performed five activities of daily living: gait, downhill walking, stair descent, squat, and sit-to-stand. Kinematics was measured both on the basis of skin markers and a mobile mono-plane fluoroscope, used to obtain the STA-free bone movement. For four different lower limb models and one corresponding to a single-body kinematics optimization (SKO), knee intersegmental moments (estimated using model-derived kinematics and ground reaction force) were compared with an estimate based on the fluoroscope. Considering all participants and activities, mean root mean square differences were the largest along the adduction/abduction axis: of 3.22Nm with a SKO approach, 3.49Nm with the three-DoF knee model, and 7.66Nm, 8.52Nm, and 8.54Nm with the one-DoF knee models. Results showed that adding joint kinematics constraints can increase the estimation errors of the intersegmental moment. These errors came directly from the errors in the estimation of the position of the knee joint centre induced by the constraints. When using a MKO approach, we recommend to analyse carefully joint centre position estimates that do not remain close to the one obtained with a SKO approach.

A penalty method for constrained multibody kinematics optimisation using a Levenberg–Marquardt algorithm

An alternative method for solving constrained multibody kinematics optimisation using a penalty method on constraints and a Levenberg–Marquardt algorithm is proposed. It is compared to an optimisation resolution with hard kinematic constraints. These methods are applied to two pairs of experiments and models. The penalty method was at least 20 times faster than the optimisation resolution while keeping similar reconstruction errors and constraints violation. The potential of the method is shown to accurately solve the multibody kinematics optimisation problem in a reasonable amount of time. A computational gain lies in implementing this resolution with a compiled and optimised program code.

Subject-specific model-derived kinematics of the shoulder based on skin markers during arm abduction up to 180° - assessment of 4 gleno-humeral joint models

Accuracy of shoulder kinematics predicted by multi-body kinematics optimisation depend on the joint models used.This study assesses the influence of four different subject-specific gleno-humeral joint models within multi-body kinematics optimisation: a 6-degree-of-freedom joint (i.e. single-body kinematics optimisation), a sphere-on-sphere joint (with two spheres of different radii) and a spherical joint with or without penalised translation. To drive these models, the 3D coordinates of 12 skin markers of 6 subjects performing static arm abduction poses up to 180° were used. The reference data was obtained using biplane X-rays from which 3D bone reconstructions were generated: scapula and humerus were 3D reconstructed by fitting a template model made of geometrical primitives on the two bones’ X-rays. Without any motion capture system, the recording of the skin markers was performed at the very same time than the X-rays with radiopaque markers.The gleno-humeral displacements and angles, and scapula-thoracic angles were computed.The gleno-humeral sphere-on-sphere joint provided slightly better results than the spherical joint with or without penalised translation, but considerably better gleno-humeral displacements than the 6-DoF joint. Considering that it can easily be personalised from medical images, this sphere-on-sphere model seems promising for shoulder multi-body kinematics optimisation.

Evaluation of a multibody kinematics optimization method for three-dimensional canine pelvic limb gait analysis

BackgroundSkin marker-based three-dimensional kinematic gait analysis were commonly used to assess the functional performance and movement biomechanics of the pelvic limb in dogs. Unfortunately, soft tissue artefact would compromise the accuracy of the reproduced pelvic limb kinematics. Multibody kinematics optimization framework was often employed to compensate the soft tissue artefact for a more accurate description of human joint kinematics, but its performance on the determination of canine pelvic limb skeletal kinematics has never been evaluated. This study aimed to evaluate a multibody kinematics optimization framework used for the determination of canine pelvic limb kinematics during gait by comparing its results to those obtained using computed tomography model-based fluoroscopy analysis.ResultsEight clinically normal dogs were enrolled in the study. Fluoroscopy videos of the stifle joint and skin marker trajectories were acquired when the dogs walked on a treadmill. The pelvic limb kinematics were reconstructed through marker-based multibody kinematics optimization and single-body optimization. The reference kinematics data were derived via a model-based fluoroscopy analysis. The use of multibody kinematics optimization yielded a significantly more accurate estimation of flexion/extension of the hip and stifle joints than the use of single-body optimization. The accuracy of the joint model parameters and the weightings to individual markers both influenced the soft tissue artefact compensation capability.ConclusionsMultibody kinematics optimization designated for soft tissue artefact compensation was established and evaluated for its performance on canine gait analysis, which provided a further step in more accurately describing sagittal plane kinematics of the hip and stifle joints.

On the Influence of the Shoulder Kinematic Chain on Joint Kinematics and Musculotendon Lengths During Wheelchair Propulsion Estimated From Multibody Kinematics Optimization.

Multibody kinematic optimization is frequently used to assess shoulder kinematics during manual wheelchair (MWC) propulsion, but multiple kinematics chains are available. It is hypothesized that these different kinematic chains affect marker tracking, shoulder kinematics, and resulting musculotendon (MT) lengths. In this study, shoulder kinematics and MT lengths obtained from four shoulder kinematic chains (open-loop thorax-clavicle-scapula-humerus (M1), closed-loop with contact ellipsoid (M2), scapula rhythm from regression equations (M3), and a single ball-and- socket joint between the thorax and the humerus (M4) were compared. Right-side shoulder kinematics from seven subjects were obtained with 34 reflective markers and a scapula locator using an optoelectronic motion capture system while propelling on a MWC simulator. Data were processed based on the four models. The results showed the impact of shoulder kinematic chains on all studied variables. Marker reconstruction errors were found to be similar between M1 and M2 and lower than for M3 and M4. Few degrees-of-freedom (DoF) were noticeably different between M1 and M2, but all shoulder DoFs were significantly affected between M1 and M4. As a consequence of differences in joint kinematics, MT lengths were affected by the kinematic chain definition. The contact ellipsoid (M2) was found as a good trade-off between marker tracking and penetration avoidance of the scapula. The regression-based model (M3) was less efficient due to limited humerus elevation during MWC propulsion, as well as the ball-and-socket model (M4) which appeared not suitable for upper limbs activities, including MWC propulsion.

A screening method to analyse the sensitivity of a lower limb multibody kinematic model

The study presents a screening method used to identify the influential parameters of a lower limb model including ligaments, at low numerical cost. Concerning multibody kinematics optimisation, the ligament parameters (isometric length) were found the most influential ones in a previous study. The screening method tested if they remain influential with minimised length variations. The most important parameters for tibiofemoral kinematics were the skin markers, segment lengths and joint parameters, including two ligaments. This was confirmed by a quantitative sensitivity analysis. The screening method has the potential to be used as a stand-alone procedure for a sensitivity analysis.

Effect of shoulder model complexity in upper-body kinematics analysis of the golf swing

The golf swing is a complex full body movement during which the spine and shoulders are highly involved. In order to determine shoulder kinematics during this movement, multibody kinematics optimization (MKO) can be recommended to limit the effect of the soft tissue artifact and to avoid joint dislocations or bone penetration in reconstructed kinematics. Classically, in golf biomechanics research, the shoulder is represented by a 3 degrees-of-freedom model representing the glenohumeral joint. More complex and physiological models are already provided in the scientific literature. Particularly, the model used in this study was a full body model and also described motions of clavicles and scapulae. This study aimed at quantifying the effect of utilizing a more complex and physiological shoulder model when studying the golf swing. Results obtained on 20 golfers showed that a more complex and physiologically-accurate model can more efficiently track experimental markers, which resulted in differences in joint kinematics. Hence, the model with 3 degrees-of-freedom between the humerus and the thorax may be inadequate when combined with MKO and a more physiological model would be beneficial. Finally, results would also be improved through a subject-specific approach for the determination of the segment lengths.

Multibody Kinematics Optimization for the Estimation of Upper and Lower Limb Human Joint Kinematics: A Systematized Methodological Review.

Multibody kinematics optimization (MKO) aims to reduce soft tissue artefact (STA) and is a key step in musculoskeletal modeling. The objective of this review was to identify the numerical methods, their validation and performance for the estimation of the human joint kinematics using MKO. Seventy-four papers were extracted from a systematized search in five databases and cross-referencing. Model-derived kinematics were obtained using either constrained optimization or Kalman filtering to minimize the difference between measured (i.e., by skin markers, electromagnetic or inertial sensors) and model-derived positions and/or orientations. While hinge, universal, and spherical joints prevail, advanced models (e.g., parallel and four-bar mechanisms, elastic joint) have been introduced, mainly for the knee and shoulder joints. Models and methods were evaluated using: (i) simulated data based, however, on oversimplified STA and joint models; (ii) reconstruction residual errors, ranging from 4 mm to 40 mm; (iii) sensitivity analyses which highlighted the effect (up to 36 deg and 12 mm) of model geometrical parameters, joint models, and computational methods; (iv) comparison with other approaches (i.e., single body kinematics optimization and nonoptimized kinematics); (v) repeatability studies that showed low intra- and inter-observer variability; and (vi) validation against ground-truth bone kinematics (with errors between 1 deg and 22 deg for tibiofemoral rotations and between 3 deg and 10 deg for glenohumeral rotations). Moreover, MKO was applied to various movements (e.g., walking, running, arm elevation). Additional validations, especially for the upper limb, should be undertaken and we recommend a more systematic approach for the evaluation of MKO. In addition, further model development, scaling, and personalization methods are required to better estimate the secondary degrees-of-freedom (DoF).

Scapular kinematic reconstruction – segmental optimization, multibody optimization with open-loop or closed-loop chains: which one should be preferred?

Several numerical approaches have been developed to address the soft tissue artefact, such as the segmental optimization and multibody kinematics optimizations using either open-loop or closed-loop chains. However, it is still not clear which method is the most efficient for scapular kinematics reconstruction. In this study, six kinematic models were compared to a silver standard, i.e. a scapula palpator, during a series of 55 postures with maximal amplitudes of both the arm and scapula performed by 15 subjects. The most accurate approaches were the multibody optimization with a closed-loop chain and the segmental optimization. They provided averaged scapula misorientations of 14.9 ± 6.7° and 16 ± 7.1, respectively. Regarding the closed-loop chain integrating a point-to-ellipsoid scapulothoracic joint, the ellipsoid providing the most accurate results was a thorax-size ellipsoid fitting the area browsed by the scapula. Eventually, considering the high implementation costs of a multibody kinematics optimization, the segmental optimization could be considered as appropriate for scapular kinematics reconstruction.

Effects of ellipsoid parameters on scapula motion during manual wheelchair propulsion based on multibody kinematics optimization. A preliminary study

Recording the motion of the scapula during manual wheelchair (MWC) propulsion is of particular interest but remains a challenge due to the soft tissue artefact. For that purpose, several authors re...

Tracking the scapula motion through multibody kinematics optimisation to study manual wheelchair propulsion

Tracking the scapula motion through multibody kinematics optimisation to study manual wheelchair propulsion

Joint kinematics estimation using a multi-body kinematics optimisation and an extended Kalman filter, and embedding a soft tissue artefact model.

To reduce the impact of the soft tissue artefact (STA) on the estimate of skeletal movement using stereophotogrammetric and skin-marker data, multi-body kinematics optimisation (MKO) and extended Kalman filters (EKF) have been proposed. This paper assessed the feasibility and efficiency of these methods when they embed a mathematical model of the STA and simultaneously estimate the ankle, knee and hip joint kinematics and the model parameters. A STA model was used that provides an estimate of the STA affecting the marker-cluster located on a body segment as a function of the kinematics of the adjacent joints. The MKO and the EKF were implemented with and without the STA model. To assess these methods, intra-cortical pin and skin markers located on the thigh, shank, and foot of three subjects and tracked during the stance phase of running were used. Embedding the STA model in MKO and EKF reduced the average RMS of marker tracking from 12.6 to 1.6mm and from 4.3 to 1.9mm, respectively, showing that a STA model trial-specific calibration is feasible. Nevertheless, with the STA model embedded in MKO, the RMS difference between the estimated and the reference joint kinematics determined from the pin markers slightly increased (from 2.0 to 2.1deg) On the contrary, when the STA model was embedded in the EKF, this RMS difference was slightly reduced (from 2.0 to 1.7deg) thus showing a better potentiality of this method to attenuate STA effects and improve the accuracy of joint kinematics estimate.

Effect of various upper limb multibody models on soft tissue artefact correction: A case study.

Soft tissue artefacts (STA) introduce errors in joint kinematics when using cutaneous markers, especially on the scapula. Both segmental optimisation and multibody kinematics optimisation (MKO) algorithms have been developed to improve kinematics estimates. MKO based on a chain model with joint constraints avoids apparent joint dislocation but is sensitive to the biofidelity of chosen joint constraints. Since no recommendation exists for the scapula, our objective was to determine the best models to accurately estimate its kinematics. One participant was equipped with skin markers and with an intracortical pin screwed in the scapula. Segmental optimisation and MKO for 24-chain models (including four variations of the scapulothoracic joint) were compared against the pin-derived kinematics using root mean square error (RMSE) on Cardan angles. Segmental optimisation led to an accurate scapula kinematics (1.1°≤RMSE≤3.3°) even for high arm elevation angles. When MKO was applied, no clinically significant difference was found between the different scapulothoracic models (0.9°≤RMSE≤4.1°) except when a free scapulothoracic joint was modelled (1.9°≤RMSE≤9.6°). To conclude, using MKO as a STA correction method was not more accurate than segmental optimisation for estimating scapula kinematics.

Kinematic models of the upper limb joints for multibody kinematics optimisation: An overview

Soft tissue artefact (STA), i.e. the motion of the skin, fat and muscles gliding on the underlying bone, may lead to a marker position error reaching up to 8.7cm for the particular case of the scapula. Multibody kinematics optimisation (MKO) is one of the most efficient approaches used to reduce STA. It consists in minimising the distance between the positions of experimental markers on a subject skin and the simulated positions of the same markers embedded on a kinematic model. However, the efficiency of MKO directly relies on the chosen kinematic model. This paper proposes an overview of the different upper limb models available in the literature and a discussion about their applicability to MKO.The advantages of each joint model with respect to its biofidelity to functional anatomy are detailed both for the shoulder and the forearm areas. Models capabilities of personalisation and of adaptation to pathological cases are also discussed. Concerning model efficiency in terms of STA reduction in MKO algorithms, a lack of quantitative assessment in the literature is noted. In priority, future studies should concern the evaluation and quantification of STA reduction depending on upper limb joint constraints.

Multibody kinematics optimization with marker projection improves the accuracy of the humerus rotational kinematics

Markers put on the arm undergo large soft tissue artefact (STA). Using markers on the forearm, multibody kinematics optimization (MKO) helps improve the accuracy of the arm kinematics especially its longitudinal rotation. However deleterious effect of STA may persist and affect other segment estimate. The objective was to present an innovative multibody kinematics optimization algorithm with projection of markers onto a requested axis of the local system of coordinates, to cancel their deleterious effect on this degree-of-freedom. Four subjects equipped with markers put on intracortical pins inserted into the humerus, on skin (scapula, arm and forearm) and subsequently on rigid cuffs (arm and forearm) performed analytic, daily-living, sports and range-of-motion tasks. Scapulohumeral kinematics was estimated using 1) pin markers (reference), 2) single-body optimization, 3) MKO, 4) MKO with projection of all arm markers and 5) MKO with projection of a selection of arm markers. Approaches 2–4 were applied to markers put on the skin and the cuff. The main findings were that multibody kinematics optimization improved the accuracy of 40–50% and the projection algorithm added an extra 20% when applied to cuff markers or a selection of skin markers (all but the medial epicondyle). Therefore, the projection algorithm performed better than multibody and single-body optimizations, especially when using markers put on a cuff. Error of humerus orientation was reduced by half to finally be less than 5°. To conclude, this innovative algorithm is a promising approach for estimating accurate upper-limb kinematics.