Feature tables and source code for Camargo et al. A Machine Learning Strategy for Locomotion Classification and Parameter Estimation using Fusion of Wearable Sensors. Transactions on Biomedical Engineering. 2021

Citation Author(s):: Jonathan Camargo (Georgia Institute of Technology)

Will Flanagan (Georgia Institute of Technology)

Noel Csomay-Shanklin (California Institute of Technology)

Bharat Kanwar (Georgia Institute of Technology)

Aaron Young (Georgia Institute of Technology)
Submitted by:: Jonathan CamargoLeyva
Last updated:: Wed, 05/18/2022 - 02:21
DOI:: 10.21227/1dmm-df06
Data Format:: .zip (included file .mat) and matlab file
Research Article Link:: A Machine Learning Strategy for Locomotion Classification and Parameter Estimat…

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Feature dataset for walking classification and parameter estimation

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Abstract

Feature tables and source code for Camargo et al. A Machine Learning Strategy for Locomotion Classification and Parameter Estimation using Fusion of Wearable Sensors. Transactions on Biomedical Engineering. 2021

Instructions:

The feature tables used for this paper can be found in ‘Classification.zip’ and ‘Regression.zip’, while source code is found in ‘CombinedLocClassAndParamEst-sourcecode.zip’. To get started, download all the files into a single folder and unzip them. Within ‘CombinedLocClassAndParamEst-master’, the folder ‘sf_analysis’ contains the main code to run, split into ‘Classification’ and ‘Regression’ code folders. There is also a 'README.md' file within the source code with more information and dependencies. If you’d like to just regenerate plots and results from the paper, then move all contents of the ‘zz_results_published’ folders (found under the feature table folders) up one folder so they are just within the ‘Classification’ or ‘Regression’ data folders. Go into the source code, find the ‘analysis’ folders, and run any ‘analyze*.m’ script with updated ‘datapath’ variables to point to the results folders you just moved.