TY - DATA
T1 - Dataset underlying the research on physical fatigue detection in running using inertial measurement units (IMUs)
PY - 2021/03/29
AU - Luca Marotta
UR - https://data.4tu.nl/articles/dataset/Dataset_underlying_the_research_on_physical_fatigue_detection_in_running_using_inertial_measurement_units_IMUs_/14307743/1
DO - 10.4121/14307743.v1
KW - biomechanics, general
KW - Machine Learning ApproachMachine
KW - imu
N2 - <div>This dataset contains data collected during fatigue detection experiments in running using IMUs.Subjects underwent a fatiguing protocol consisting of three distinct consecutive runs on an athletic track:</div><div>1.	The first run consisted of a 4000 m run (10 laps) at a constant speed, determined as 100% of the average speed of the subject during the best performance in the previous year  on a 5 to 10 km race;</div><div>2.	The second run was performed according to a fatiguing protocol. The speed in this fatiguing protocol started at the same level of the first run and increased progres-sively of by 0.2km/h every 100 m. Perceived fatigue was assessed by means of a Borg Rating of Perceived Extertion (RPE) Scale (min-max score 6-20) [20], asked to the runner every 100 m. The fatiguing protocol was terminated once the RPE was higher than   16 (RPE between hard and very hard)  , or, if such requirement was not met, after 1200m;</div><div>3.	The third run consisted of a 1200m run (3 laps), in which speed was kept constant and equal to the first 4000 m run.</div><div><br></div><div><br></div><div>pXXX_XXX_0-2K: contains the Segment and Joint data exported from MVN for the first half of the first run</div><div>pXXX_XXX_2-4K: contains the Segment and Joint data exported from MVN for the second half of the first run</div><div>pXXX_XXX_postfatigue1200m: : contains the Segment and Joint data exported from MVN for the third run</div><div><br></div><div>pXXX_strides: contains the segmented strides from each subject</div><div><br></div><div><br></div><div>TableFeats: contains values used for the machine learning pipeline, after normalization over each single subject</div>
ER -