Non-invasive assessment of soft-tissue artefacts in hip joint kinematics using motion capture data and ultrasound depth measurements

Australia and New Zealand Clinical Motion Analysis Group (ANZ-CMAG) clinical practice recommendations

Clinical motion analysis involves quantitative measurement of gait patterns to identify gait anomalies that currently or have the potential to impact function, activities of daily living and participation. Clinical motion analysis services are equipped with motion capture technology and comprise specialised staff who deliver 3-dimensional motion analysis services to children and adults who present with varying levels of gait impairment. Data is then used to inform intervention recommendations to clinicians with a view to maintaining independent, functional and pain free walking (or appropriate mobility). The ANZ-CMAG (established in 2013) identified a need to establish recommendations to assist in standardising practice guidelines for both current and new clinical motion analysis services within the region. The group serves to promote collaboration between services in quality assurance processes, clinical practices, data sets and research activities. The clinical practice recommendations described in this paper cover: i) requirements for a motion analysis service (including staffing, facilities and equipment), ii) patient assessments (requirements, clinical information and data gathered, reporting and interpretation of patient data), iii) quality assurance processes (including motion capture system / biomechanical models & limitations, marker placement, data storage / record keeping, creation of normative dataset); iv) helpful resources. Better outcomes for children and adults with gait deviations is dependent upon accurate measurement and evaluation of walking and requires input from multidisciplinary clinical teams with specialist knowledge and skills. The ANZ-CMAG hopes these clinical practice recommendations are beneficial to motion analysis services with an aim to improve clinical practices, patient outcomes, and support research collaboration.

A joint kinematics driven model of the pelvic soft tissue artefact

When skin-markers trajectories are used in human movement analysis, compensating for their relative movement with respect to the underlying bone (soft tissue artefact, STA) is essential for accurate bone-pose estimation; information about the artefact is required in the form of a mathematical model. Such model, not available for pelvic artefacts, could allow pelvic STA compensation in routine gait analysis by embedding it in skeletal kinematics estimators and developing ad-hoc optimization problems for the estimate of subject-specific model parameters. It was developed as driven by adjacent body segment kinematics. Model architecture feasibility was tested; its compensation effectiveness was assessed evaluating the error in pelvic orientation after removing the modelled artefact from the measured one. Five volunteers with a wide body mass range (BMI: 22-37) underwent MRI scans to reconstruct subject-specific pelvic digital bone models. Multiple anatomical calibrations performed in different static postures, as occurring during walking and star-arc movements, registering the bone-models with points digitized through stereophotogrammetry over pelvic bony prominences, allowed to define the relevant poses of a pelvis-embedded anatomical coordinate system. Such approach allowed to measure STAs over several pelvic anatomical landmarks, for each posture and subject. Model parameters were estimated by minimizing the least squares difference between measured and modelled STAs. The measured STAs were appropriately modelled with subject-specific calibrations, both in terms of shape (correlation coefficient: median [inter-quartile-range]: 0.72 [0.36]) and amplitude (root mean square residual: 3.0 [3.2] mm). Consequently, the overall error in pelvic orientation vector (5.1 [4.4] deg) was reduced after removing the modelled artefacts (2.5 [1.9] deg).

Center of pressure based segment inertial parameters validation

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.

Greater trochanter location measurement using a three-dimensional motion capture system during prone hip extension

[Purpose] The greater trochanter (GT) is an important structure in biomedical research, but the measurement methods require development. This study presents data from a new measurement method that does not use GT-marker-based measurement (No GT-m) in comparison with GT-marker based measurement (GT-m). [Subjects and Methods] We recruited 20 healthy subjects, who were asked to perform and maintain a prone position and then move to the prone hip extension. A motion capture system collected the kinematic data and the location of the GT was calculated by two measurements. [Results] GT migration distance differed significantly between the two measurements and the coefficient of the variation value was lower for the No GT-m method. Thigh lengths of the No GT-m method were comparable to the original lengths. There were significant differences between the GT-m and the other methods. [Conclusions] These data suggest that the GT-m method yielded a lower precision with a smaller GT migration distance. In the comparison of thigh length, the No GT-m method was in close agreement with the original length. We suggest that determining the location of the GT using the No GT-m has greater accuracy than the GT-m method.