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Using FitJointCentre.m in Nexus 2
Examples
1. Running the sphere fit algorithm on hip functional calibration after Nexus OCST rigid body
segment tracking
a. Calibrate OCST for Pelvis in static trial
b. Calibrate OCST for LFemur in static trial
c. Process OCST for Pelvis and LFemur in dynamic trial 2 OCST Bones in model
outputs
d. Run Matlab Operation FitJointCentre.m with arguments:
‘Pelvis’,’LFemur’,’Geom’,{’LTHAP’,’LTHAD’,’LTHLD’,’LTHI’} (or any set of markers
affixed to the thigh segment)
2. Running the CTT (centre transformation technique) algorithm on hip functional calibration
after Nexus OCST rigid body segment tracking
Same a. b. c. as above with
d. Run Matlab Operation FitJointCentre.m with arguments: ‘Pelvis’,’LFemur’,’CTT’
From my experience the CTT algorithm lead to the exact same results as the SCORE
algorithm (see [1, 2]) and in the picture below. There are 3 ‘Modeled Markers’ in the
workspace, the L_HJC_Geom and L_HJC_CTT from FitJointCentre.m and
Pelvis_LFemur_score from the Nexus Score operation. Pelvis_LFemur_score is highlighted
with a white edge and L_HJC_CTT is exactly on the same position. It is selected so it is
colored in blue and you can notice some blue parts. L_HJC_Geom is in a different position
and in grey color.
3. Running the sphere fit algorithm on hip functional calibration movement captured with the
PiG marker set.
Since PiG femur has less than 3 tracking markers, it is theoretically impossible to track the
distal rigid body segment and therefore fit a CTT/SCORE centre on hip functional calibration
captured with the PiG markerset. However, one can fit a sphere fit L_HJC centre by providing
the segments ‘PEL’ and ‘LFE’ as proximal and distal segments and the ‘LTHI’ and ‘LKNE’
markers (this means PiG dynamic has to be run beforehand).
Run Matlab Operation with arguments: ‘PEL’,’LFE’,’Geom’,{’LTHI’,’LKNE’} (default) or
‘PEL’,’LFE’,’Geom’,’LTHI’
However you need to keep in mind that the sphere fit algorithm is much more reliable when
it fits a set of concentric spheres (several markers) rather than only one, like the example
above, or the centroid of the marker set.
The interpretation of Merr, the return value, need to be careful when the proximal and/or
distal segment did not originate from a rigid body kinematics tracking (i.e. when coming
from PiG).
References
1. Sangeux M, Peters A, Baker R. Hip joint center localization: evaluation on normal subjects in
the context of gait analysis. Gait & posture. 2011;34:324-8.
2. Sangeux M, Pillet H, Skalli W. Which method of hip joint centre localisation should be used in
gait analysis? Gait & posture. 2014;40(1):20-5.
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Accurate localization of the hip joint centre is required to obtain accurate kinematics, kinetics and musculoskeletal modelling results. Literature data showed that conclusions drawn from synthetic data, adult normal subjects and cerebral palsy children may vary markedly. This study investigated the localization accuracy of the hip joint centre against EOS. The EOS system allowed us to register the hip joint centres with respect to the skin markers on standing subjects. A comprehensive set of predictive and functional calibration techniques were tested. For the functional calibration techniques, our results showed that algorithm, range of motion and self-performance of the movement were factors significantly affecting the results. Best results were obtained for comfortable range and self-performance of the movement. The best method in this scenario was the functional geometrical sphere fitting method which localized the hips 1.1 cm from the EOS reference in average and 100% of the time within 3 cm. Worst results for functional calibration methods occurred when the movement was assisted with a reduced range of movement. The best method in this scenario was the Harrington et al. regression equations since it does not rely on a functional calibration movement. Harrington et al. equations put the hips 1.7 cm from the EOS reference in average and 97% of the time within 3 cm. We conclude that accurate localization of the hip joint centre is possible in gait analysis providing that method to localize the hip joint centres are adapted to the population studied: functional geometrical sphere fitting when hip calibration movements are not a problem and Harrington et al. predictive equations otherwise
Article
Full-text available
Locating the position of the hip joint centre (HJC) is an important part of lower limb modeling for gait analysis. Regression equations have been used in the past but a range of functional calibration methods are now available. This study compared the accuracy of HJC localization from two sets of regression equations and five different functional calibration methods against three dimensional ultrasound (3-DUS) on a population of 19 able bodied subjects. Results show that the geometric sphere fitting technique was the best performer with mean absolute distance error of 15mm and 85% of measurements being within 20mm. The results also show that widely used regression equations perform particularly badly whereas the most recent equations performed very closely to the best functional method with a mean absolute error of 16mm and 88% of measurements being within 20mm. In vivo results are more than an order of magnitude worse than predictions using synthetic data suggesting that additional work is required before soft tissue artifact can be effectively modelled.