1. Menzies Health Institute Queensland
An alternative marker set to accurately and reliably quantify joint kinematics during load carriage
Gavin Lenton1,2, Tim Doyle, PhD3, Dan Billing, PhD2, David Lloyd, PhD1
1Centre for Musculoskeletal Research, Griffith University, Gold Coast, Australia 2Land Division, Defence Science and Technology Group, Melbourne, Australia 3Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia
Background & Aims
Determine the accuracy and repeatability of joint kinematics with and without body armour
Compare the accuracy and repeatability of our new marker set against a conventional marker set
Conclusions
Virtual method provides a viable alternative for researchers to reliably measure joint kinematics when equipment (e.g., body armour) obscures marker placement
Kinematics involves the measurement of the position and time components of human motion. Camera systems track markers placed over anatomical landmarks and reconstruct the three- dimensional position and orientation of body segments in space1.
Body armour, however, occludes bony landmarks at the torso and pelvis, preventing direct marker placement
Previous studies using body armour simplified the kinematic model2, modified the equipment3, or placed markers over the armour4. However, these methods may not accurately derive joint kinematics from the trunk and pelvis. This study proposed a new marker set to use when body armour (or other equipment) occludes markers at the pelvis and torso.
New marker set showed good accuracy and repeatability compared with conventional marker set without armour
New marker set superior to placing markers over armour in both accuracy and repeatability
Cannot reliably obtain joint kinematics with markers placed over armour
Results compare well with previous repeatability studies7
Consistent marker placement remains critical for collecting reliable kinematics
Results
(n= 8)
Physical marker set
Virtual marker set
Sagittal
Frontal
Transverse
No Armour
Within-day CMC
Hip
0.99 (0.01)
0.94 (0.03)
0.92 (0.06)
Pelvis
0.74 (0.10)
0.95 (0.04)
0.84 (0.05)*
0.90 (0.14)
Trunk
0.81 (0.08)
0.98 (0.01)
0.87 (0.06)
0.97 (0.01)*
Between-day CMC
0.97 (0.03)
0.97 (0.02)
0.87 (0.09)
0.97 (0.04)
0.81 (0.16)
0.47 (0.27)
0.95 (0.05)
0.89 (0.17)
0.49 (0.22)
0.93 (0.07)
0.84 (0.14)
0.58 (0.29)
0.97 (0.07)
0.43 (0.30)
0.92 (0.07)
0.90 (0.09)
Armour
0.89 (0.09)
0.91 (0.07)
0.71 (0.09)
0.89 (0.08)
0.76 (0.16)
0.85 (0.12)
0.68 (0.14)
0.82 (0.09)*
0.96 (0.03)
0.96 (0.04)
0.78 (0.19)
0.96 (0.02)
0.77 (0.20)
0.28 (0.12)
0.86 (0.14)
0.42 (0.13)
0.88 (0.10)
0.73 (0.20)
0.41 (0.09)
0.83 (0.15)
0.51 (0.16)
0.81 (0.21)
0.91 (0.09)
Methods z x y X O Y Z o s2 s3 s1
3-D printed a custom-designed sacral cluster that defined the position of virtual pelvis marker
11-camera motion capture system (Vicon, Oxford, UK) collected three-dimensional kinematic (100Hz) data as participants walked on a treadmill (AMTI, Watertown, USA)
Virtual marker set
Physical marker set
Marker position
Physical marker set
Virtual marker set
Suprasternal notch
Marker
Pointer
7th Cervical vertebra
Cluster
8th Thoracic vertebra
1st Sacral vertebra
Anterior superior iliac spine
Posterior superior iliac spine
Bland-Altman analysis compared the accuracy between marker sets,
The coefficient of multiple correlations6 determined within- and between- session repeatability (> 0.80 indicates good repeatability), and inter-protocol kinematic similarities
Results
(n = 8)
Inter-protocol CMC
Sagittal
Frontal
Transverse
No Armour
Hip
0.99 (0.01)
0.97 (0.03)
0.79 (0.22)
Pelvis
0.63 (0.14)
0.97 (0.02)
0.81 (0.14)
Trunk
0.74 (0.11)
0.96 (0.04)
0.90 (0.09)
Armour
0.96 (0.03)
0.95 (0.02)
0.70 (0.07)
0.08 (0.21)
0.91 (0.04)
0.65 (0.34)
0.16 (0.31)
0.89 (0.06)
0.81 (0.22)
(n = 7)
Hip
Pelvis
Trunk
Mean
d (SD)
95% LOA
No Armour
Joint ROM
Sagittal
46.7
–2.6 (4.9)
–12.1 → 7.0
4.4
–1.2 (2.2)
–5.4 → 3.1
4.8
–3.1 (1.3)
–0.5 → 5.7
Frontal
17.5
–2.3 (3.0)
–8.2 → 3.6
11.4
–1.0 (2.1)
–5.0 → 3.1
13.6
–1.5 (2.6)
–3.6 → 6.5
Transverse
15.6
1.3 (1.3)
–5.3 → 7.8
10.9
1.9 (1.9)
–1.1 → 5.0
17.2
2.3 (2.7)
–7.6 → 2.9
Armour
47.4
–1.9 (3.2)
–8.2 → 4.3
4.3
–2.6 (2.2)
–7.0 → 1.8
5.0
–2.3 (2.5)
–7.3 → 2.7
15.8
–1.5 (1.2)
–3.9 → 0.8
9.8
–1.9 (1.3)
–4.6 → 0.7
10.8
–1.2 (2.9)
–6.9 → 4.5
18.1
–0.9 (3.8)
–8.4 → 6.6
10.7
0.0 (1.8)
–3.5 → 3.5
14.5
–0.4 (2.6)
–5.6 → 4.7
References
Leardini, Chiari, Croce, & Cappozzo. (2005). Human movement analysis using stereophotogrammetry: Part 3. Soft tissue artifact assessment and compensation. Gait Posture, 21(2),
Attwells, Birrell, Hooper, & Mansfield. (2006). Influence of carrying heavy loads on soldiers' posture, movements and gait. Ergonomics, 49(14),
Caron, Lewis, Saltzman, Wagenaar, & Holt. (2015). Musculoskeletal stiffness changes linearly in response to increasing load during walking gait.
Majumdar, Pal, & Majumdar. (2010). Effects of military load carriage on kinematics of gait. Ergonomics, 53(6),
Ferrari, Cutti, & Cappello. (2010). A new formulation of the coefficient of multiple correlation to assess the similarity of waveforms measured synchronously by different motion analysis protocols. Gait Posture, 31(4),
Borhani, McGregor, & Bull. (2013). An alternative technical marker set for the pelvis is more repeatable than the standard pelvic marker set. Gait Posture, 38(4),
Mean, mean of both methods; d (SD), mean difference (standard deviation of difference) between the marker sets (physical – virtual); 95% LOA, lower and upper limits of agreement at ± 1.96 SD
Menzies Health Institute Queensland
menzies.griffith.edu.au
Centre for Musculoskeletal Research, School of Allied Health Science, Griffith University