Determination of most important predictor of Vertical Ground Reaction Force (VGRF) and threshold value of fracture among young adults

Abstract

Background: Lower-limb stress fracture is one of the most common type of injuries among soldiers worldwide. Understanding the kinetics and physiological responses of military footwear has immense importance increasing mobility, performance/combat readiness and decreasing risk of footwear related injuries. Objectives: In the present study key factor of fractures and limiting value of fractures wearing combat boots of different weight [heavy weight (HW) and light weight (LW)] was investigated using bioinformatics approaches. Method: Twenty (n=20) Indian Infantry Soldiers with mean ± SD age, height and weight of 35 (±6.0)years, 181.3(±5.1)cm, 82.5 (±5.2) kg, respectively, volunteered for the study. This study has used Artificial Neural Network (ANN) for entropy measurement to predict the relationship of input variables of stance phase of gait cycle (Heel strike, toe off, heel strike to toe off duration, braking force, propulsive force, appearance time of braking force and propulsive force) with the output variables [Coefficient of friction (CoF) & impulse]. Results: Based on the outcomes it appears a better and authentic explanation to consider duration of heel strike to toe off value of a complete gait cycle to evaluate Impulse as a measure of fractural injuries. Likewise appearance time of braking force can be used to evaluate Coefficient of frictional movements as a measure of fall and risk injuries. Conclusion: Limiting value of VGRF for fracture was found to be 792N. Risks of metatarsal fractures were found to be higher in HW boots as the VGRF value is closer to fracture value at same walking speed.

Keywords

• ann
• data mining
• fracture
• footwear
• injuries
• vgrf