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

Abstract


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.

Keywords


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

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References


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

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