Dataset of knee joint contact force peaks and corresponding subject characteristics from 4 open datasets

This dataset contains data from overground walking trials of 166 subjects with several trials per subject (approximately 2900 trials total).

DATA ORIGINS & LICENSE INFORMATION

The data comes from four existing open datasets collected by others:

Schreiber & Moissenet, A multimodal dataset of human gait at different walking speeds established on injury-free adult participants

• article: https://www.nature.com/articles/s41597-019-0124-4
• dataset: https://figshare.com/articles/dataset/A_multimodal_dataset_of_human_gait_at_different_walking_speeds/7734767

Fukuchi et al., A public dataset of overground and treadmill walking kinematics and kinetics in healthy individuals

• article: https://peerj.com/articles/4640/
• dataset: https://figshare.com/articles/dataset/A_public_data_set_of_overground_and_treadmill_walking_kinematics_and_kinetics_of_healthy_individuals/5722711

Horst et al., A public dataset of overground walking kinetics and full-body kinematics in healthy adult individuals

• article: https://www.nature.com/articles/s41598-019-38748-8
• dataset: https://data.mendeley.com/datasets/svx74xcrjr/3

Camargo et al., A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions

• article: https://www.sciencedirect.com/science/article/pii/S0021929021001007
• dataset (3 links): https://data.mendeley.com/datasets/fcgm3chfff/1 https://data.mendeley.com/datasets/k9kvm5tn3f/1 https://data.mendeley.com/datasets/jj3r5f9pnf/1

In this dataset, those datasets are referred to as the Schreiber, Fukuchi, Horst, and Camargo datasets, respectively.
The Schreiber, Fukuchi, Horst, and Camargo datasets are licensed under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).

We have modified the datasets by analyzing the data with musculoskeletal simulations & analysis software (OpenSim).
In this dataset, we publish modified data as well as some of the original data.

STRUCTURE OF THE DATASET
The dataset contains two kinds of text files: those starting with "predictors_" and those starting with "response_".

Predictors comprise 12 text files, each describing the input (predictor) variables we used to train artifical neural networks to predict knee joint loading peaks.
Responses similarly comprise 12 text files, each describing the response (outcome) variables that we trained and evaluated the network on.
The file names are of the form "predictors_X" for predictors and "response_X" for responses, where X describes which response (outcome) variable is predicted with them.
X can be:
- loading_response_both: the maximum of the first peak of stance for the sum of the loading of the medial and lateral compartments
- loading_response_lateral: the maximum of the first peak of stance for the loading of the lateral compartment
- loading_response_medial: the maximum of the first peak of stance for the loading of the medial compartment
- terminal_extension_both: the maximum of the second peak of stance for the sum of the loading of the medial and lateral compartments
- terminal_extension_lateral: the maximum of the second peak of stance for the loading of the lateral compartment
- terminal_extension_medial: the maximum of the second peak of stance for the loading of the medial compartment
- max_peak_both: the maximum of the entire stance phase for the sum of the loading of the medial and lateral compartments
- max_peak_lateral: the maximum of the entire stance phase for the loading of the lateral compartment
- max_peak_medial: the maximum of the entire stance phase for the loading of the medial compartment
- MFR_common: the medial force ratio for the entire stance phase
- MFR_LR: the medial force ratio for the first peak of stance
- MFR_TE: the medial force ratio for the second peak of stance

The predictor text files are organized as comma-separated values. Each row corresponds to one walking trial. A single subject typically has several trials.
The column labels are DATASET_INDEX,SUBJECT_INDEX,KNEE_ADDUCTION,MASS,HEIGHT,BMI,WALKING_SPEED,HEEL_STRIKE_VELOCITY,AGE,GENDER.

• DATASET_INDEX describes which original dataset the trial is from, where {1=Schreiber, 2=Fukuchi, 3=Horst, 4=Camargo}
• SUBJECT_INDEX is the index of the subject in the original dataset. If you use this column, you will have to rewrite these to avoid duplicates (e.g., several datasets probably have subject "3").
• KNEE_ADDUCTION is the knee adduction-abduction angle (positive for adduction, negative for abduction) of the subject in static pose, estimated from motion capture markers.
• MASS is the mass of the subject in kilograms
• HEIGHT is the height of the subject in millimeters
• BMI is the body mass index of the subject
• WALKING_SPEED is the mean walking speed of the subject during the trial
• HEEL_STRIKE_VELOCITY is the mean of the velocities of the subject's pelvis markers at the instant of heel strike
• AGE is the age of the subject in years
• GENDER is an integer/boolean where {1=male, 0=female}

The response text files contain one floating-point value per row, describing the knee joint contact force peak for the trial in newtons (or the medial force ratio). Each row corresponds to one walking trial.
The rows in predictor and response text files match each other (e.g., row 7 describes the same trial in both predictors_max_peak_medial.txt and response_max_peak_medial.txt).

See our journal article "Prediction of Knee Joint Compartmental Loading Maxima Utilizing Simple Subject Characteristics and Neural Networks" (https://doi.org/10.1007/s10439-023-03278-y) for more information.

Questions & other contacts: jere.lavikainen@uef.fi