Methods / Statistics
This study aims to investigate the efficacy of a stacking approach to estimate parameters in treadmill running. Nineteen participants ran on a treadmill at self-selected pace. Ground reaction force and kinematic data were collected. Stacking in machine learning was used to estimate the peak vertical ground reaction force and step time. Good agreement was observed in the test data set for predicted and measured values of the peak vertical ground reaction force component and step time where the ICC values were 0.85 and 0.99 respectively. This suggests stacking may be a feasible approach to estimate kinetic and kinematic parameters during treadmill running.
Ong, Alex Dr; Harris, Ian Sujae Dr; and Hamill, Joseph Dr
"ESTIMATING THE PEAK VERTICAL GROUND REACTION FORCE COMPONENT AND STEP TIME IN TREADMILL RUNNING USING MACHINE LEARNING - A PILOT STUDY,"
ISBS Proceedings Archive: Vol. 38:
1, Article 90.
Available at: https://commons.nmu.edu/isbs/vol38/iss1/90