Following data collection, all joint kinematic and kinetic data was processed using Qualysis Track Manager Software where each marker was identified, labelled and digitised (figure 3.9).
Figure 3.9– Qualysis Track Manager Figure 3.10 Visual 3D Model
All successful trials were then exported to Visual 3D (V3D) (figure 3.10) software (version 4.91, C-Motion Inc, Rockville, MD, USA). Dynamic skeletal graphics created in V3D (figure 3.10) using the marker set depicted in figure 3.7, controlled by subject kinematics were used to assist with the interpretation of results (Buczek et al., 2010). A V3D model comprises of a collection of rigid segments, each of which relates to a subjects particular body segment (major bone structures). The position and orientation of a segment with six variables is known as a segment POSE (3 variables describe the position of the origin, 3 variables describe the rotation) in 3-D space, normally 3 variables describe the segment translation in three perpendicular axes (vertical, medial-lateral and anterior-posterior), and 3 variables describe the rotation about each axes of the segment (sagittal, frontal and transverse). The positions of reflective markers are translated into the pose of the corresponding model, identified using motion tracking equipment by V3D (Visual 3D, 2015). The motion-tracking apparatus tracks the reflective markers which are applied to specific locations on or near the subject’s body, and not the actual body segments. The body segments which are tracked are defined by proximal and distal endpoints located inside the subject’s body (Visual 3D, 2015). As mentioned previously, markers and sensors can be placed inside the subject’s body, however for this study, markers were attached over bony (anatomical) landmarks, on each subject’s skin.
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The model is referred to as a six degree of freedom (DOF) model due to having six variables that describe its position and orientation in 3-D space (3 variables describe segment translation in three orthogonal axes, and 3 variables describe the rotation about each axis). Individual subjects anthropometric measurements (height and body mass) were entered into the software to calculate the length and the centre of mass of the segment for use in kinetic data analysis. Pelvis, thigh, leg and foot segments were then modelled using anatomical landmarks or joint centres and the radius of the proximal and distal end of the segment and the tracking markers, with the interpretation of results (Buczek et al., 2010).
The VISUAL3D model segments and tracking markers are detailed in table 3.2.
Table 3.2: Visual 3D building of the biomechanical model segments.
Segment Proximal radius/joint Distal radius/joint Tracking markers
Pelvis
- Right anterior superior iliac spine
- Left anterior superior iliac spine
- Right posterior superior iliac spine
- Left posterior superior iliac spine
Pelvis cluster pad (4 tracking markers) Left and right anterior posterior iliac spine
Thigh
- Hip joint centre* - Greater trochanter
- Medial femoral condyle - Lateral femoral condyle
Thigh cluster pad (4 tracking markers)
Shank
- Medial femoral condyle - Lateral femoral condyle
- Medial malleolus - Lateral malleolus
Shank cluster pad (4 tracking markers)
Foot
- Medial malleolus - Lateral malleolus
- 1st metatarsal head (in barefoot and shod) - 5th metatarsal head (in
barefoot and shod)
Superior/inferior calcaneus, medial/lateral calcaneus (in shod) Heel cup cluster (4 markers) (barefoot) Hallux in barefoot and shod
* Hip joint centre is automatically calculated by using anterior and posterior superior iliac spine markers using the regression equation by Bell and Brand (1990)
The maximum gaps of marker data was filled with polynomial interpolation algorithms. Motion and force plate data was filtered using a Butterworth 4th order bi-directional low pass filter with
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cut off frequencies of 6Hz for kinematics as used previously by Winter (2009) and 25Hz for kinetics as used previously by Schneider and Chao (1983) based on a residual analysis (Yu et al., 1999). Joints kinematics were calculated using an X-Y-Z Euler rotation sequence, where X represented flexion/extension, Y adduction/adduction or varus/valgus, and Z internal/external rotation. Joint kinetic data were calculated using 3-D inverse dynamics and the joint moment data was normalised to body mass and presented as external moments referenced to proximal segment. Automatic gait event definition was utilised in all trials, which captured data when the vertical GRF exceeded 20 Newtons (N) in value. The gait cycle was defined as the movement and events from heel strike of the foot on the force platform, to the subsequent heel strike of the same foot. Stance phase was defined as heel strike of the foot to the subsequent toe-off of the same foot. Each gait parameter of interest for each of the studies was then exported from V3D to Microsoft Excel 2010 (Microsoft Washington, USA) for further analysis.
The thesis aims to gain a more thorough understanding of loading at the knee joints in both healthy individuals and knee OA patients, considering the effects of wearing lateral wedge insoles (LWI) on the loading at the knee joint. For such a study to be accomplished sufficiently, the reliability of the investigator must be assessed prior to the collection of the study data. A test-retest reliability study was therefore conducted in order to enable the researcher to appreciate the measurement error present in the results, which would entirely arise from the placement of the reflective markers.