Dataset underlying the research on physical fatigue detection in running using inertial measurement units (IMUs)
doi: 10.4121/14307743.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future.
For a link that will always point to the latest version, please use
doi: 10.4121/14307743
doi: 10.4121/14307743
Datacite citation style:
Marotta, Luca (2021): Dataset underlying the research on physical fatigue detection in running using inertial measurement units (IMUs). Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/14307743.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
usage stats
1528
downloads
1066
views
4
shares
1
citations
licence

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:
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;
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;
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.
pXXX_XXX_0-2K: contains the Segment and Joint data exported from MVN for the first half of the first run
pXXX_XXX_2-4K: contains the Segment and Joint data exported from MVN for the second half of the first run
pXXX_XXX_postfatigue1200m: : contains the Segment and Joint data exported from MVN for the third run
pXXX_strides: contains the segmented strides from each subject
TableFeats: contains values used for the machine learning pipeline, after normalization over each single subject
history
- 2021-03-29 first online, published, posted
publisher
4TU.ResearchData
organizations
University of Twente;Roessingh Research and Development
DATA
files
- 210,585,396 bytes md5 p001_HDSL_CCW_0-2k.mat
- 204,569,236 bytes md5 p001_HDSL_CW_2-4k.mat
- 133,180,613 bytes md5 p001_HDSL_postfatigue1200m.mat
- 104,003,675 bytes md5 p001_strides.mat
- 244,296,077 bytes md5 p002_HDSL_CCW_0-2k.mat
- 253,398,219 bytes md5 p002_HDSL_CW_2-4k.mat
- 150,563,304 bytes md5 p002_HDSL_postfatigue1200m.mat
- 128,335,672 bytes md5 p002_strides.mat
- 296,422,428 bytes md5 p003_HDSL_CCW_2-4k.mat
- 282,021,653 bytes md5 p003_HDSL_CW_0-2k.mat
- 172,446,486 bytes md5 p003_HDSL_postfatigue1200m.mat
- 134,708,273 bytes md5 p003_strides.mat
- 268,573,185 bytes md5 p004_HDSL_CCW_0-2k.mat
- 268,719,813 bytes md5 p004_HDSL_CW_2-4k.mat
- 158,946,260 bytes md5 p004_HDSL_postfatigue1200m.mat
- 135,186,573 bytes md5 p004_strides.mat
- 272,130,130 bytes md5 p005_HDSL_CCW_0-2k.mat
- 269,666,739 bytes md5 p005_HDSL_CW_2-4k.mat
- 164,873,651 bytes md5 p005_HDSL_postfatigue1200m.mat
- 129,770,199 bytes md5 p005_strides.mat
- 219,299,070 bytes md5 p006_HDSL_CCW_2-4k.mat
- 221,218,527 bytes md5 p006_HDSL_CW_0-2k.mat
- 135,407,708 bytes md5 p006_HDSL_postfatigue1200m.mat
- 110,005,120 bytes md5 p006_strides.mat
- 258,722,186 bytes md5 p007_HDSL_CCW_0-2k.mat
- 258,480,817 bytes md5 p007_HDSL_CW_2-4k.mat
- 126,892,320 bytes md5 p007_strides.mat
- 220,590,519 bytes md5 p008_HDSL_CCW_2-4k.mat
- 220,330,509 bytes md5 p008_HDSL_CW_0-2k.mat
- 136,295,535 bytes md5 p008_HDSL_postfatigue1200m.mat
- 106,881,970 bytes md5 p008_strides.mat
- 14,982,536 bytes md5 TableFeats.mat
- 3,006 bytes md5 README.txt
- download all files (zip)