Machine learning-based classification model corresponding to the paper 'Machine learning to improve orientation estimation in sports situations challenging for inertial sensor use'

doi:10.4121/14883927.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: 10.4121/14883927
Datacite citation style:
van Dijk, Marit (2021): Machine learning-based classification model corresponding to the paper 'Machine learning to improve orientation estimation in sports situations challenging for inertial sensor use'. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/14883927.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Software
Machine learning model as used in 'Machine learning to improve orientation estimation in sports situations challenging for inertial sensor use'. The model to run the Extended Madgwick filter is based on a random forest algorithm and can be used as explained in Figure 3 in the paper.
Explanation for use:The model can be loaded and executed in Python using the following code- RFmodel = pickle.load(open([filename_RFmodel], 'rb'))- y = RFmodel.predict_proba(X)[:,1]- if y > 0.5 EFcorrect = 1 else EFcorrect = 0 X are (normalized) input variablesy are probabilities for EFcorrect (see paper)
history
  • 2021-07-13 first online, published, posted
publisher
4TU.ResearchData
funding
  • ZonMw project number: 546003002
organizations
TU Delft, Faculty of Mechanical, Maritime and Materials Engineering, department of BioMechanical Engineering
Vrije Universiteit Amsterdam
The Hague University of Applied Sciences

DATA

files (1)