Predicting Ground Reaction Force from a Hip-Borne Accelerometer during Load Carriage
| Autor: | Michael E. LaFiandra, Jennifer M. Neugebauer |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Adult
Male Acceleration Physical Therapy Sports Therapy and Rehabilitation Walking Accelerometer Body Mass Index Weight-Bearing Wearable Electronic Devices Young Adult 03 medical and health sciences 0302 clinical medicine Statistics Humans Orthopedics and Sports Medicine Ground reaction force Treadmill Mathematics Load carriage Hip Equipment Design 030229 sport sciences Actigraphy Biomechanical Phenomena Shoes Vertical acceleration Activity monitor Regression Analysis 030217 neurology & neurosurgery |
| Zdroj: | Medicine & Science in Sports & Exercise. 50:2369-2374 |
| ISSN: | 1530-0315 0195-9131 |
| DOI: | 10.1249/mss.0000000000001686 |
| Popis: | INTRODUCTION Ground reaction forces (GRF) during load carriage differ from unloaded walking. Methods to quantify peak vertical GRF (pGRFvert) of Soldiers walking with loads outside of a laboratory are needed to study GRF during operationally relevant tasks. PURPOSE Develop a statistically based model to predict pGRFvert during loaded walking from ActiGraph GT3X+ activity monitor (AM) vertical acceleration. METHODS Fifteen male Soldiers (25.4 ± 5.3 yr, 85.8 ± 9.2 kg, 1.79 ± 9.3 m) wore an ActiGraph GT3X+ AM over their right hip. Six walking trials (0.67-1.58 m·s) with four loads (no load, 15, 27, 46 kg) and two types of footwear (athletic shoes and combat boots) were completed on an instrumented force plate treadmill. Average peak vertical AM acceleration (pACCvert) and pGRFvert were used to develop a regression equation to predict pGRFvert. The model was validated using a leave-one-subject-out approach. Root mean square error (RMSE) and average absolute percent difference (AAPD) between actual and predicted pGRFvert were determined. pGRFvert was also predicted for two novel data sets and AAPD and RMSE calculated. RESULTS The final equation to predict pGRFvert included pACCvert, body mass, carried load mass, and pACCvert-carried load mass interaction. Cross-validation resulted in an AAPD of 4.0% ± 2.7% and an RMSE of 69.5 N for leave-one-subject-out and an AAPD of 5.5% ± 3.9% and an RMSE of 78.7 N for the two novel data sets. CONCLUSION A statistically based equation developed to predict pGRFvert from ActiGraph GT3X+ AM acceleration proved to be accurate to within 4% for Soldiers carrying loads while walking. This equation provides a means to predict GRF without a force plate. |
| Databáze: | OpenAIRE |
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