Can optimal marker weightings improve thoracohumeral kinematics accuracy?

Influence of marker weights optimization on scapular kinematics estimated with a multibody kinematic optimization

Scapular kinematic estimates are altered by soft tissue artefacts, therefore experimental and numerical methods should be developed to improve their accuracy. This study aimed to assess the influence of weights applied to the scapula markers within a closed-loop multibody kinematic optimization on scapular kinematic estimates. Fifteen healthy volunteers performed static postures mimicking analytical, daily living and sport movements. Scapulo-thoracic angles were computed either from a scapula locator as the reference, or from a closed-loop multibody-kinematic optimization (MKO) including a participant-specific point-on-ellipsoid scapulothoracic joint. Weights applied to scapula markers in the MKO were optimized to minimize the difference in scapular orientation from the reference. Optimizing weighting sets significantly (p < 0.0001) improved scapular orientation from 0.9° to 12.1° in comparison to scapular kinematics estimated with non-optimized weighting sets. The mean optimized weighting set contained no neglectable weight for all markers from the acromion to the medial border of the scapular spine but showed no significant difference (p = 0.547) compared to homogeneous weights. Optimized weighting sets were participant- and movement- specific. To conclude, homogenous weights applied on redundant markers located from acromion to scapular medial border spine are recommended when estimating scapular kinematics in upper limb MKO.

Comparison of a Scaled Cadaver-Based Musculoskeletal Model With a Clinical Upper Extremity Model

Reliably and accurately estimating joint/segmental kinematics from optical motion capture data has remained challenging. Studies objectively characterizing human movement patterns have typically involved inverse kinematics and inverse dynamics techniques. Subsequent research has included scaled cadaver-based musculoskeletal (MSK) modeling for noninvasively estimating joint and muscle loads. As one of the ways to enhance confidence in the validity of MSK model predictions, the kinematics from the preceding step that drives such a model needs to be checked for agreement or compared with established/widely used models. This study rigorously compares the upper extremity (UE) joint kinematics calculated by the Dutch Shoulder Model implemented in the AnyBody Managed Model Repository (involving multibody kinematics optimization (MKO)) with those estimated by the Vicon Plug-in Gait model (involving single-body kinematics optimization (SKO)). Ten subjects performed three trials of (different types of) reaching tasks in a three-dimensional marker-based optical motion capture laboratory setting. Joint angles, processed marker trajectories, and reconstruction residuals corresponding to both models were compared. Scatter plots and Bland-Altman plots were used to assess the agreement between the two model outputs. Results showed the largest differences between the two models for shoulder, followed by elbow and wrist, with all root-mean-squared differences less than 10 deg (although this limit might be unacceptable for clinical use). Strong-to-excellent Spearman's rank correlation coefficients were found between the two model outputs. The Bland-Altman plots showed a good agreement between most of the outputs. In conclusion, results indicate that these two models with different kinematic algorithms broadly agree with each other, albeit with few key differences.

The effects of anatomical errors on shoulder kinematics computed using multi-body models

Joint motion calculated using multi-body models and inverse kinematics presents many advantages over direct marker-based calculations. However, the sensitivity of the computed kinematics is known to be partly caused by the model and could also be influenced by the participants' anthropometry and sex. This study aimed to compare kinematics computed from an anatomical shoulder model based on medical images against a scaled-generic model and quantify the effects of anatomical errors and participants' anthropometry on the calculated joint angles. Twelve participants have had planar shoulder movements experimentally captured in a motion lab, and their shoulder anatomy imaged using an MRI scanner. A shoulder multi-body dynamics model was developed for each participant, using both an image-based approach and a scaled-generic approach. Inverse kinematics have been performed using the two different modelling procedures and the three different experimental motions. Results have been compared using Bland-Altman analysis of agreement and further analysed using multi-linear regressions. Kinematics computed via an anatomical and a scaled-generic shoulder models differed in average from 3.2 to 5.4 degrees depending on the task. The MRI-based model presented smaller limits of agreement to direct kinematics than the scaled-generic model. Finally, the regression model predictors, including anatomical errors, sex, and BMI of the participant, explained from 41 to 80% of the kinematic variability between model types with respect to the task. This study highlighted the consequences of modelling precision, quantified the effects of anatomical errors on the shoulder kinematics, and showed that participants' anthropometry and sex could indirectly affect kinematic outcomes.

Compensating for Soft-Tissue Artifact Using the Orientation of Distal Limb Segments During Electromagnetic Motion Capture of the Upper Limb

Most motion capture measurements suffer from soft-tissue artifacts (STA). Especially affected are rotations about the long axis of a limb segment, such as humeral internal-external rotation (HIER) and forearm pronation-supination (FPS). Unfortunately, most existing methods to compensate for STA were designed for optoelectronic motion capture systems. We present and evaluate a STA compensation method that 1) compensates for STA in HIER and/or FPS, 2) is developed specifically for electromagnetic motion capture systems, and 3) does not require additional calibration or data. To compensate for STA, calculation of HIER angles rely on forearm orientation, and calculation of FPS angles rely on hand orientation. To test this approach, we recorded whole-arm movement data from eight subjects and compared their joint angle trajectories calculated according to progressive levels of STA compensation. Compensated HIER and FPS angles were significantly larger than uncompensated angles. Although the effect of STA compensation on other joint angles (besides HIER and FPS) was usually modest, significant effects were seen in certain DOF under some conditions. Overall, the method functioned as intended during most of the range of motion of the upper limb, but it becomes unstable in extreme elbow extension and extreme wrist flexion-extension. Specifically, this method is not recommended for movements within 20° of full elbow extension, full wrist flexion, or full wrist extension. Since this method does not require additional calibration of data, it can be applied retroactively to data collected without the intent to compensate for STA.

Feasibility of an alternative method to estimate glenohumeral joint center from videogrammetry measurements and CT/MRI of patients

Videogrammetry is commonly used to record upper limb motions. However, it cannot track the glenohumeral joint center (GH). GH is required to reconstruct upper limb motions. Therefore, it is often estimated by separately measuring scapular motions using scapular kinematics measurements devices (SKMD). Applications of SKMD are neither straightforward nor always noninvasive. Therefore, this work investigates the feasibility of an alternative method to estimate GH from videogrammetry using a CT/MRI image of subject's glenohumeral joint and without requiring SKMD. In order to evaluate the method's accuracy, its GH estimations were compared to reference GH trajectories. The method was also applied to estimate scapular configurations and reconstruct an abduction motion measured by videogrammetry. The accuracy of GH estimations were within 5 mm, and the reconstructed motion was in good agreement with reported in vivo measurements.

Tracking Joint Angles During Whole-Arm Movements Using Electromagnetic Sensors

Electromagnetic (EM) motion tracking systems are suitable for many research and clinical applications, including in vivo measurements of whole-arm movements. Unfortunately, the methodology for in vivo measurements of whole-arm movements using EM sensors is not well described in the literature, making it difficult to perform new measurements and all but impossible to make meaningful comparisons between studies. The recommendations of the International Society of Biomechanics (ISB) have provided a great service, but by necessity they do not provide clear guidance or standardization on all required steps. The goal of this paper was to provide a comprehensive methodology for using EM sensors to measure whole-arm movements in vivo. We selected methodological details from past studies that were compatible with the ISB recommendations and suitable for measuring whole-arm movements using EM sensors, filling in gaps with recommendations from our own past experiments. The presented methodology includes recommendations for defining coordinate systems (CSs) and joint angles, placing sensors, performing sensor-to-body calibration, calculating rotation matrices from sensor data, and extracting unique joint angles from rotation matrices. We present this process, including all equations, for both the right and left upper limbs, models with nine or seven degrees-of-freedom (DOF), and two different calibration methods. Providing a detailed methodology for the entire process in one location promotes replicability of studies by allowing researchers to clearly define their experimental methods. It is hoped that this paper will simplify new investigations of whole-arm movement using EM sensors and facilitate comparison between studies.

A survey of human shoulder functional kinematic representations

In this survey, we review the field of human shoulder functional kinematic representations. The central question of this review is to evaluate whether the current approaches in shoulder kinematics can meet the high-reliability computational challenge. This challenge is posed by applications such as robot-assisted rehabilitation. Currently, the role of kinematic representations in such applications has been mostly overlooked. Therefore, we have systematically searched and summarised the existing literature on shoulder kinematics. The shoulder is an important functional joint, and its large range of motion (ROM) poses several mathematical and practical challenges. Frequently, in kinematic analysis, the role of the shoulder articulation is approximated to a ball-and-socket joint. Following the high-reliability computational challenge, our review challenges this inappropriate use of reductionism. Therefore, we propose that this challenge could be met by kinematic representations, that are redundant, that use an active interpretation and that emphasise on functional understanding.

Effect of tibia marker placement on knee joint kinematic analysis

Variability of kinematic measures determined by different marker sets among sites participating in a collaborative study is necessary for determining the reliability of a multi-site gait analysis research. We compared knee kinematics based on different marker sets on the tibia, calculating by segmental optimization (SO) and multi-body optimization (MBO) methods respectively, in order to assess the effect of marker locations on the methods. 11 healthy subjects participated in the study with 33 markers attached to the lower extremity segments, and 4 groups were identified according to markers on the tibia. Knee joint kinematics during level walking were measured and then compared among the 4 groups using statistical parametric mapping. For SO method, the results showed that there were no significant differences in the knee joint angles when used different marker sets on the tibia. However, significant differences were found in the transverse plane kinematics for MBO method. It was concluded that MBO method was more likely to be influenced by different marker sets. More attention should be paid to marker sets, specifically for MBO method, when three-dimensional gait analysis data are shared and interpreted among sites for clinical decision-making.

Rigid and non-rigid geometrical transformations of a marker-cluster and their impact on bone-pose estimation

When stereophotogrammetry and skin-markers are used, bone-pose estimation is jeopardised by the soft tissue artefact (STA). At marker-cluster level, this can be represented using a modal series of rigid (RT; translation and rotation) and non-rigid (NRT; homothety and scaling) geometrical transformations. The NRT has been found to be smaller than the RT and claimed to have a limited impact on bone-pose estimation. This study aims to investigate this matter and comparatively assessing the propagation of both STA components to bone-pose estimate, using different numbers of markers. Twelve skin-markers distributed over the anterior aspect of a thigh were considered and STA time functions were generated for each of them, as plausibly occurs during walking, using an ad hoc model and represented through the geometrical transformations. Using marker-clusters made of four to 12 markers affected by these STAs, and a Procrustes superimposition approach, bone-pose and the relevant accuracy were estimated. This was done also for a selected four marker-cluster affected by STAs randomly simulated by modifying the original STA NRT component, so that its energy fell in the range 30-90% of total STA energy. The pose error, which slightly decreased while increasing the number of markers in the marker-cluster, was independent from the NRT amplitude, and was always null when the RT component was removed. It was thus demonstrated that only the RT component impacts pose estimation accuracy and should thus be accounted for when designing algorithms aimed at compensating for STA.