Data from: The effect of sampling methods on the validity and reliability of the estimation of the orbital stability of human gait
Data files
Aug 25, 2025 version files 3.16 GB
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README.md
9.26 KB
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Supplementary_Data.zip
3.16 GB
Abstract
This repository provides open-source data and code for analyzing orbital stability of human gait using Floquet Multipliers (FMs). Included are raw experimental data, simulation data, processed results, and Python scripts used for the analysis.
The full methodology and results are detailed in the following publication:
š https://doi.org/10.1098/rsos.250106
š Folder Structure
Supplementary_Data
ā
āāā Code # Python scripts for data analysis
ā āāā FM_Clinical_Experiment.py
ā āāā FM_Simulation.py
ā āāā FM_Functions.py
ā
āāā Data
ā
āāā RawData # Walking experiment data
ā āāā WalkingData.joblib
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āāā RawData_Results # Intermediate and result files generated from walking experiment data
ā āāā OverallData
ā ā āāā Mean_Overall_Con_idx.joblib
ā āāā sub1
ā ā āāā DF_FMs_Trial.joblib
ā ā āāā FM_Profile_of_Walking_Experiment.png
ā ā āāā Jacobians_Trials.joblib
ā ā āāā SVs_Trials.joblib
ā ā āāā Target_FM.joblib
ā āāā ...
ā āāā sub20
ā
āāā Simulation_Results # Intermediate and result files generated from simulation data
āāā OverallData
ā āāā DF_CI_Diff.csv
ā āāā DF_CI_Real.csv
ā āāā DF_CI_Simul.csv
ā āāā LMM_Data.csv
ā āāā Overall_ICC_data.joblib
ā āāā TargetFM_Real.joblib
ā āāā TargetFM_Simul.joblib
āāā sub1
ā āāā DF_FMs_Simuls1000.joblib
ā āāā Exp100_Bias.joblib
ā āāā Exp100_Corr.joblib
ā āāā Sim_Target_FM.joblib
ā āāā SimDatas1000.joblib # Simulation Data
āāā ...
āāā sub20
š Contents
| File / Folder | Description |
|---|---|
FM_Clinical_Experiment.py |
Script for calculating FMs and generating figures from walking experiment data |
FM_Simulation.py |
Script for simulating gait data using representative Jacobian matrices |
FM_Functions.py |
Script containing supporting functions |
WalkingData.joblib |
Walking experiment data |
Mean_Overall_Con_idx.joblib |
Result file showing the average condition number from five-trial walking experiment data for each subject |
DF_FMs_Trial.joblib |
Intermediate file containing FM estimates across sample sizes and trials |
FM_Profile_of_Walking_Experiment.png |
Result file showing the figure generated from DF_FMs_Trial.joblib |
Jacobians_Trials.joblib |
Intermediate file containing representative Jacobian matrices |
SVs_Trials.joblib |
Intermediate file containing the design matrix of state vectors |
Target_FM.joblib |
Intermediate file containing the average maximum FM from five-trial walking experiment data |
DF_CI_Diff.csv |
Result file showing the differences between average FM estimates from walking and simulation data for each subject |
DF_CI_Real.csv |
Result file showing the average FM estimates from five-trial walking experiment data for each subject |
DF_CI_Simul.csv |
Result file showing the average FM estimates from five-trial simulation data for each subject |
LMM_Data.csv |
Intermediate file for linear mixed model analysis |
Overall_ICC_data.joblib |
Intermediate file for intraclass correlation analysis |
TargetFM_Real.joblib |
Intermediate file containing the mean target FM across all subjects from five-trial walking experiment data |
TargetFM_Simul.joblib |
Intermediate file containing the mean target FM across all subjects from five-trial simulation data |
DF_FMs_Simuls1000.joblib |
Intermediate file containing FM estimates across sample sizes and trials from simulation |
Exp100_Bias.joblib |
Intermediate file containing the bias of FM estimates across sample sizes and trials from simulations |
Exp100_Corr.joblib |
Intermediate file for Pearson's correlation analysis |
Sim_Target_FM.joblib |
Intermediate file containing the maximum FM of the representative Jacobian matrix |
SimDatas1000.joblib |
Simulation Data |
š Data Descriptions
1. Walking Experiement Data (WalkingData.joblib)
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The .joblib extension indicates a binary file format
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Task: Level treadmill walking at 1.17 m/s
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Trials: 5 independent 10-minute walking trials per participant
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Rest: 5-minute rest between each trial
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Participants:
- N = 20
- Age: 25 ± 4.5 years
- Height: 176.8 ± 4.6 cm
- Mass: 73.0 ± 5.0 kg
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Sampling Rate: 100 Hz
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Duration: 10 minutes per trial
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Columns:
Left_ankle_x,Left_ankle_y,Left_ankle_z,Right_ankle_x,Right_ankle_y,Right_ankle_z
Left_knee_x,Left_knee_y,Left_knee_z,Right_knee_x,Right_knee_y,Right_knee_z,
Left_hip_x,Left_hip_y,Left_hip_z,Right_hip_x,Right_hip_y,Right_hip_z,
Left_Dist_x,Left_Dist_y,Left_Dist_z,Right_Dist_x,Right_Dist_y,Right_Dist_z,
Left_toe_x,Left_toe_y,Left_toe_z,Right_toe_x,Right_toe_y,Right_toe_z
Left_heel_x,Left_heel_y,Left_heel_z,Right_heel_x,Right_heel_y,Right_heel_z -
Columns Explanation: Hip, knee, and ankle joint angles; distances between the pelvis center and the heels; marker positions of the toes and heels
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Units: Degrees for joint angles; meters for marker positions
2. Simulation Data (SimDatas1000.joblib)
- Task: Simulation of joint angle errors across 1,000 strides
- Trials: 1,000 independent time series
- Stride length: 1,000 strides per time series
- Noise: Multivariate Gaussian noise (zero mean) applied per stride
- Initialization: Random initial state for each trial
- Columns: The same as in
WalkingData.joblib - Units: Degrees
āļø Python Scripts
1. Environment Requirements
- Python 3.9
- Required packages:
pandas,numpy,scipy,scikit-learn,matplotlib,joblib,pingouin, etc.
2. Execution Instructions
- Walking Experiment Data Analysis
RunFM_Clinical_Experiment.pyto:- Calculate Floquet Multipliers
- Extract Jacobian matrices from experimental data
- Generate relevant figures
- Simulation Analysis
RunFM_Simulation.pyto:- Generate synthetic gait data using the Jacobians
- Create simulation plots
- Supporting Funtions
FM_Functions.py: Contains all supporting functions used by the above scripts
3. Notes
- All scripts use relative paths ā no file path adjustment is required.
- The
DataGenerationflag is set toFalseby default.
To save time, use the provided pre-generated data. - All intermediate and result files can be generated from the RawData.
š Citation
If you use this dataset or code, please cite the following publication:
Moon, J., et al. (2025).
The effect of sampling methods on the validity and reliability of the estimation of the orbital stability of human gait.
Royal Society Open Science.
š https://doi.org/10.1098/rsos.250106
