Load distribution model underlying the publication: Towards an accurate rolling resistance: Estimating intra-cycle load distribution between front- and rear wheels during wheelchair propulsion

doi: 10.4121/c533f919-1a44-48d5-8543-5c7f8be29bb0.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/c533f919-1a44-48d5-8543-5c7f8be29bb0
Datacite citation style:
van Dijk, Marit; Heringa, L.H.A.; de Vette, Vera; Hoozemans, Marco J. M.; Berger, Monique et. al. (2024): Load distribution model underlying the publication: Towards an accurate rolling resistance: Estimating intra-cycle load distribution between front- and rear wheels during wheelchair propulsion. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/c533f919-1a44-48d5-8543-5c7f8be29bb0.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset

Based on the 'dataset of the front-wheel load of a set of wheelchair propulsion experiments' in https://doi.org/10.4121/bc9a8588-5e50-4dff-aa77-5114ff7626f7, a machine learning model is trained. The model, and the python-code to run the model on acquired kinematic data, is attached.


Wheelchair propulsion experiments were executed on a treadmill. During the treadmill sessions, front wheel load was assessed with load pins to determine the load distribution between the front and rear wheels. Accordingly, a machine learning model was trained to predict load distribution from kinematic data of the wheelchair and trunk. Input of the model was data of two inertial sensors (one attached to the trunk and one attached to the wheelchair) and output of the model was the relative front wheel load (or 'The load on the front wheels is expressed as percentage of the total weight (of wheelchair user/athlete + wheelchair)'.

history
  • 2024-01-17 first online, published, posted
publisher
4TU.ResearchData
format
.docx, .py
funding
  • WheelPower: wheelchair sports and data science push it to the limit (grant code 546003002) [more info...] ZonMw
organizations
- Delft University of Technology, Faculty of Mechanical, Maritime and Materials Engineering (3mE), Department of BioMechanical Engineering
- Vrije Universiteit Amsterdam, Department of Human Movement Sciences
- The Hague University of Applied Sciences

DATA

files (2)