DOI: 10.15125/bath-00965
title: Dataset for "Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running"

description: This dataset includes the input features and target labels needed to train and test FootNet. The input features include the distal tibia anteroposterior velocity, ankle plantar/dorsi flexion angle and foot centre of mass anteroposterior and vertical velocities. Additionally,  ground reaction force data and trial names are also included.
description: This dataset includes data coming from five different datasets collected in three independent laboratories (see associated publication for more details). It includes treadmill running kinematics and kinetics processed to obtain the previously mentioned variables and chopped in running gait cycles.
description: The original datasets were fully reprocessed as described in the Methods section of the associated publication.
description: The project directory StepDetectionStudy is organised as follows:

  - Data > OriginalDatasets: Folder containing the entire datasets (*_dataset.npy files).

  - Data > DataFolds.npy: File containing the training data grouped in 5 folds.

  - Data > TestingSet.npy: File containing the testing set.

    Data are organised as Python dictionaries containing the kinematic input features ['X'], label vectors ['Y'], metadata about the trials ['meta'] and vertical GRF ['GRFv']. Each of those dictionary keys contains a list with nested lists with the structure participant > trial > stride. For instance, `dataset['X'][0][0][0]` accesses the kinematic input features characterising the first stride recorded in the first trial of the first participant in dataset.

  - CrossValidation > Models: Folder containing the five models developed during cross validation.

  - CrossValidation > Results: Folder containing the summary performance metrics for each model on its corresponding validation set and Bland-Altman plots comparing foot strike, toe off and contact times as predicted by FootNet vs gold standard method.

  - FinalTest > FootNet_best_candidate: Folder containing the best set of parameters resulting from cross validation.

    Summary performance metrics on testing set and Bland-Altman plots comparing foot strike, toe off and contact times as predicted by FootNet vs gold standard method.

  - FinalTest > y_and_yhat.mat: File containing testing predictions, target labels and metadata from testing stride cycles for posterior analyses in Matlab presented in the paper.

  - FinalModel: Folder containing the final updated model resulting from FinalTest as a SavedModel directory (Tensorflow model format) and as .h5.

  - Notebooks > TrainTest_Split.ipynb: Google Colab (Jupyter) notebook demonstrating how the dataset splitting was performed, including training and testing (70/30) and further folding of training dataset in 5 folds.

  - Notebooks > CrossValidation.ipnyb. Google Colab (Jupyter) notebook that performs 5-fold cross-validation and selects the best set of weights as best candidate for the final test.

  - Notebooks > FinalTest.ipnyb: Google Colab (Jupyter) notebook that updates the best candidate model resulting from cross-validation with the 5 folds as training set and performs the final test on the testing set.