SoftwareApplication
identifier: 10.4121/14883927
name: Machine learning-based classification model corresponding to the paper 'Machine learning to improve orientation estimation in sports situations challenging for inertial sensor use'
description: Machine learning-based classification model corresponding to the paper 'Machine learning to improve orientation estimation in sports situations challenging for inertial sensor use'
description: 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)
author: https://orcid.org/0000-0002-4900-8084
keywords: orientation estimation
keywords: sports
keywords: Machine learning
keywords: kinematics
keywords: Inertial Measurement Unit
keywords: Madgwick filter
license:
  creative work: creativeWork
    name: CC-BY-SA-4.0
reference publication: fspor.2021.670263

https://orcid.org/0000-0002-4900-8084
type: Person
given name: Marit
family name: van Dijk
name: Marit van Dijk

fspor.2021.670263
type: ScholarlyArticle
name: Machine Learning to Improve Orientation Estimation in Sports Situations Challenging for Inertial Sensor Use
url: https://doi.org/10.3389%2Ffspor.2021.670263