Dataset of the front-wheel load of a set of wheelchair propulsion experiments

doi: 10.4121/bc9a8588-5e50-4dff-aa77-5114ff7626f7.v2
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/bc9a8588-5e50-4dff-aa77-5114ff7626f7
Datacite citation style:
van Dijk, Marit; Heringa, Louise; Veeger, DirkJan HEJ; Hoozemans, Marco J. M.; Berger, Monique et. al. (2024): Dataset of the front-wheel load of a set of wheelchair propulsion experiments. Version 2. 4TU.ResearchData. dataset.
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
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version 2 - 2024-01-17 (latest)
version 1 - 2023-11-03

Twenty-five participants (19 females, mean age (S.D) = 30 (10) years, mean body mass = 68 (11) kg, height = 170 (7) cm) with no wheelchair experience were included in the study. Participants propelled the hand-rims of a wheelchair on a large (3.0 x 5.0 m) motor-driven treadmill, while their kinematics were measured with three IMUs (attached to the participants’ sternum, the wheelchair’s frame, and right wheel axle) and the front-wheel load was measured using custom-made load pins (in both front wheel axes). Before the treadmill sessions, participants received a 10-minute overground wheelchair training to get familiar with the wheelchair and a 10-minute training on the treadmill (see Fig. 1). After three treadmill sessions, drag tests were performed on the treadmill to obtain rolling resistance coefficients of the (small) front and (large) rear wheels.

To simulate different wheelchair characteristics and push styles, the treadmill session was repeated six times with different tire pressures (1.75 bar, 3.5 bar, 5.25 bar) or added mass (0 kg, 5 kg, 15 kg), see Fig. 1, and with three pushing styles (no trunk motion at 1.2 m/s [style 1], normal trunk motion at 1.2 m/s [style 2], normal trunk motion at 1.7 m/s [style 3]). By following a metronome (25 beats/min in pushing style 1 and 40 beats/min in pushing style 2 and 3), participants were stimulated to make long pushes accompanied by ‘natural’ trunk motion. Each treadmill session consisted of 30s familiarization to the new situation, after which participants propelled 60s in each pushing style. In this way, a dataset was composed of eighteen (three push styles and six treadmill sessions) 60s-time trials per participant. The order of the treadmill sessions differed per participant.

The load on the front wheels is expressed as percentage of the total weight (of participant + wheelchair).

The dataset consists of 11 columns representing the following variables

v_wc: linear velocity of the wheelchair in m/s

a_wc: linear acceleration of the wheelchair in m/s^2

av_tr: Angular velocity of trunk (around sagittal axis) in rad/s

aa_tr: Angular acceleration of trunk (around sagittal axis) in rad/s2

ang_tr: Trunk inclination angle in rad

laz_tr: Trunk acceleration perpendicular to the frontal plane of the trunk in m/s2

lay_tr: Trunk caudal-cranial acceleration in m/s2

lar_tr: Magnitude of trunk acceleration vector in m/s2

F: front wheel-load as percentage of the total weight (of participant + wheelchair)

subjectnr: subject number

blocknr: block number in which

block 1: rear wheel tyre pressure = 5.25; added mass = 0 kg (practice/familiarization session)

block 2: rear wheel tyre pressure = 5.25; added mass = 5 kg

block 3: rear wheel tyre pressure = 5.25; added mass = 15 kg

block 4: rear wheel tyre pressure = 5.25; added mass = 0 kg

block 5: rear wheel tyre pressure = 3.50; added mass = 0 kg

block 6: rear wheel tyre pressure = 1.75; added mass = 0 kg

See also the file 'additional information.pdf'.

  • 2023-11-03 first online
  • 2024-01-17 published, posted
  • WheelPower: wheelchair sports and data science push it to the limit (grant code 546003002) [more info...] ZonMw
- 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


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