Quantifying Forearm Soft Tissue Motion from Massless Skin Markers following Forward Fall Hand Impacts

Danielle L. Gyemi, Don Clarke, Paula M. van Wyk, William J. Altenhof, David M. Andrews


Background: Investigating soft tissue motion related to impact events is important for understanding how the body mitigates potentially injurious forces through shock attenuation. Objectives: The aims of this study were to: 1) quantify displacement and velocity of the forearm soft tissues following forward fall impacts; and 2) compare two massless skin marker designs (single layer, uniform (SLU) design; stacked, non-uniform (SNU) design) in terms of how well they could be tracked over varying skin pigmentations using automated motion capture software. Methods: Two participant groups (skin pigmentation: light – 9F, 8M; dark – 9F, 6M) underwent simulated forward fall hand impacts for each marker design using a torso-release apparatus. Marker positions associated with planar motion of forearm soft tissues during impact were automatically tracked (ProAnalyst®) in the proximal-distal and anterior-posterior axes from high speed recordings (5000 f/s). Mean peak displacements and velocities for eight forearm regions were then calculated (LabVIEW®). Results: Overall, soft tissue displacement and velocity increased from distal to proximal forearm regions. The greatest displacement (1.47 cm) and velocity (112.8 cm/s) occurred distally toward the wrist. Soft tissue impact responses between sexes did not differ, on average (p > 0.05). The SLU and SNU markers produced different kinematic values (p < 0.05); however, the magnitudes of, and consequently meaningfulness of these statistical differences for automatically tracking soft tissue motion, were negligible (displacement: ≤ 0.05 cm; velocity: ≤ 2.5 cm/s). Conclusions: Forearm soft tissue motion was successfully quantified for forward fall hand impacts; both marker designs were deemed functionally equivalent.


Upper Extremity, Forearm, Accidental Falls, Biomechanical Phenomena, Pattern Recognition, Automated

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DOI: https://doi.org/10.7575/aiac.ijkss.v.6n.3p.1


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