Data for: Detecting artificially impaired balance in human locomotion: metrics, perturbation effects and detection thresholds

Measuring balance is important for detecting impairments and developing interventions to prevent falls, but there is no consensus on which method is most effective. Many balance metrics derived from steady-state walking data have been proposed, such as step width variability, step time variability, foot placement predictability, maximum Lyapunov exponent, and margin of stability. Recently, perturbation-based metrics such as center of mass displacement have also been explored. Perturbations typically involve unexpected disturbances applied to the subject. In this study, we collected walking data from 10 healthy subjects while walking normally and impairing their balance with ankle braces, eye-blocking masks, and pneumatic jets on their legs. In some walking trials, we also applied mechanical perturbations to their pelvis. We provide a comprehensive biomechanics dataset as supplementary material. We compared the ability of various metrics to detect impaired balance using steady-state walk..., , # Steady-state and perturbed walking dataset

Dataset DOI: [10.5061/dryad.cnp5hqch3](10.5061/dryad.cnp5hqch3)

## Description of the data and file structure

1\) <\"Scripts\" folder> section: Describes the folder titled \"Scripts\", which includes information on how we processed our data.

2\) <\"Subject X\" folders...> section: Describes all the data in the folders that can be found in the \"Subject X\" folders.

3\) <Additional details...> section: Detailed description of the labels in each of the .mat data files and other important information about the dataset.

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### \"Scripts\" folder

Scripts and aggregate data used for time-synchronizing sensors (e.g., between Vicon, EMG, etc.) and sensor data processing.

Read the README in the \"Scripts\" folder for a more detailed guide on how we processed our data using these scripts.

visualizeProcessedData.m demonstrates how to load important signals in the dataset using MATLAB.

Note: All code using the data in this dataset to generate results in ..., We confirm that we obtained explicit written consent from all participants to publish their de-identified data in the public domain as part of the study’s approved IRB protocol. To ensure privacy, all identifying information was removed, and each participant was assigned a random numerical code. The dataset includes only biomechanical and sensor data with no names, dates of birth, or other personally identifiable information.