%0 Generic
%A van Dijk, Marit
%A Heringa, L.H.A.
%A de Vette, Vera
%A Hoozemans, Marco J. M.
%A Berger, Monique
%A Veeger, DirkJan H.E.J.
%D 2024
%T Load distribution model underlying the publication: Towards an accurate rolling resistance: Estimating intra-cycle load distribution between front- and rear wheels during wheelchair propulsion
%U 
%R 10.4121/c533f919-1a44-48d5-8543-5c7f8be29bb0.v1
%K Rolling resistance
%K inertial measurement unit
%K wheelchair propulsion
%K load distribution
%K mechanical power
%K Wheelchair sports
%K Treadmill data
%X <p>Based on the 'dataset of the front-wheel load of a set of wheelchair propulsion experiments' in <a href="https://doi.org/10.4121/bc9a8588-5e50-4dff-aa77-5114ff7626f7" target="_blank">https://doi.org/10.4121/bc9a8588-5e50-4dff-aa77-5114ff7626f7</a>, a machine learning model is trained. The model, and the python-code to run the model on acquired kinematic data, is attached.</p><p><br></p><p>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)'.</p>
%I 4TU.ResearchData