ESTIMATING THE PEAK VERTICAL GROUND REACTION FORCE COMPONENT AND STEP TIME IN TREADMILL RUNNING USING MACHINE LEARNING - A PILOT STUDY

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

ESTABLISHING A METHOD TO DETERMINE IMPACT FORCE IN TENNIS WITH DIFFERENT STRING TENSIONS – A PRELIMINARY STUDY

The purpose of this study was to establish a method to estimate impact force in tennis forehand stroke to determine if differences in string tension would affect impact force. This is a preliminary study using only one participant. Estimates were determined using kinematic data and data obtained from strain gauges. Preliminary data on peak resultant impact force estimates were within the range of those reported in the literature. Peak resultant force estimates were larger for higher string tension rackets and lower string tension in the racquets possibly due to differences in coefficient of restitution. Data estimated from this study, regardless of string tension, may give a better representative of peak resultant impact force as the data were not filtered. Increasing the number of participants or the number of trials will be needed to confirm this preliminary finding