DOI: 10.5061/dryad.3tx95x6rb
title: Data from: Probability of lateral instability while walking on winding paths

description: Gait biomechanics is most often studied during straight-ahead walking.
 However, real-life walking imposes many turns and/or other such maneuvers
 that people must navigate.  Such maneuvers challenge people’s
 lateral balance. In older adults or others with impaired walking, such
 tasks can induce falls, which can increase risk of injuries in these
 populations.  Therefore, determining how people’s lateral balance
 is impacted during these more complex walking tasks, and how they adapt
 their steps, is critical.  Here, we asked 24 young healthy adult
 participants (12F/12M; Age 25.8±3.5yrs) to walk on both wide and narrow
 virtual paths that were either straight, slowly-winding, or
 quickly-winding. This data set comprises their lower body, pelvis, and
 head kinematics as they walked along those paths. A file of participant
 characteristics (.xlsx), including group demographics, participant
 anthropometrics, and assessment scores is also provided. In addition, a
 marker-set definition file (.xlsx) is also provided. These data include
 how people navigate paths of different width and curviness, which may lend
 themselves to several applications such as investigations of more
 real-world gait interventions to target adaptive strategies that could
 more effectively improve mobility.
description: This experiment included data from 24 healthy human adult
 participants (12F/12M; Age 25.8±3.5yrs).  Data regarding their baseline
 demographics, relevant participant anthropometrics, and relevant
 assessment scores are provided (*.xlsx file).
 Participants walked on a motorized treadmill in a Motek M-Gait
 virtual reality system (https://www.motekmedical.com/). The walking paths they walked on are described in detail in the associated README file.  Each participant completed two experimental trials (4 min long each) walking on each of 6 different types of paths. For each trial performed by each participant, motion capture data were recorded with a 10-camera Vicon system (https://www.vicon.com/).  These data were cleaned using Vicon Nexus software, and further processed in Matlab (https://www.mathworks.com/). All marker trajectories and path data (treadmill distance) are provided in this data set.
description: # Data: Healthy Human Adults Walking on Winding Paths
 [https://doi.org/10.5061/dryad.3tx95x6rb](https://doi.org/10.5061/dryad.3tx95x6rb) ## Description of the data and file structure Here, we asked 24 young healthy human adult participants (12F/12M; Age 25.8±3.5yrs) to walk on both wide and narrow virtual paths that were either straight, slowly-winding, or quickly-winding. Each participant's age, height, body mass and leg length (greater trochanter to lateral malleolus) were recorded.  Each participant also completed a four-choice reaction time test (4CRT) and four-square step test (FSST). Participants walked on a motorized treadmill in a Motek M-Gait virtual reality system ([https://www.motekmedical.com/](https://www.motekmedical.com/)).  Each participant completed two experimental trials (4 min long each) walking on each of 6 different types of paths.  Three pseudo-randomly oscillating paths were created from a sum of three sin waves with incommensurate frequencies: z(x)= 0.22 sin(A·0.46875x) + 0.05 sin(A·0.625x) + 0.03 sin(A·0.9375x) where z is the lateral position (in meters) of the path center, A is a frequency scaling factor, and x is forward treadmill distance (in meters) starting at -0.55 m due to the projection of the path relative to the origin of the treadmill.  Path Frequency Scaling Factors were A = 0 for straight (STR) paths, A = 1 for slowly-winding (LOF) paths, and A = 4 for quickly-winding (HIF) paths.  Each of these 3 path shapes was presented at each of 2 Path Widths:  Wide (W) = 0.60m and Narrow (N) = 0.30m. For each trial performed by each participant, motion capture data were recorded with a 10-camera Vicon system ([https://www.vicon.com/](https://www.vicon.com/)).  These data were cleaned using Vicon Nexus software, and further processed in Matlab ([https://www.mathworks.com/](https://www.mathworks.com/)). All marker trajectories and path data (treadmill distance) are provided in this data set. ### Files and variables #### File: README.txt **Description:**  Information about the study, data files and their structure and variables included.  Contains additional specifics and details beyond the descriptions given in this Dryad ReadMe. #### File: WindingPaths\_Participant-Info.xlsx **Description:**  Participant characteristics and baseline test scores ##### Variables * Group Age (years)  [Mean ± SD & Range]  * Group Body Mass (kg)  [Mean ± SD & Range] * Group Body Height (m)  [Mean ± SD & Range] * Group Body Mass Index (BMI; kg/m^2)  [Mean ± SD & Range] * Individual Participant Sex (F/M) * Individual Participant Leg Length (m) * Individual Participant Four Square Step Test (FSST) Times (s) * Individual Participant Four-Choice Reaction Time (4CRT) Accuracy Rates (%) * Individual Participant Four-Choice Reaction Time (4CRT) Mean Reaction Times (s) #### File: WindingPaths\_MarkerSetKey.xlsx **Description:**  Definitions of anatomical landmarks where motion capture markers were placed on each participant. ##### Variables * Marker # * Marker Label * Marker Description #### File: WindingPaths\_Kinematics\_ACCL\_n24.mat **Description:**  Kinematic (motion capture) marker data for Acclimation ("ACCL") trials performed by each participant.  Marker data recorded at 100 Hz. ##### Variables * Video Frame # * Time [s] * Treadmill distance ("TMDist") [m] * 3D (XYZ) coordinates of 44 reflective markers #### File: WindingPaths\_Kinematics\_HIFN\_n24.mat **Description:** Kinematic (motion capture) marker data (100 Hz) for all walking trials performed by each participant on the High-Frequency Narrow (HIFN) walking paths. ##### Variables * Video Frame # * Time [s] * Treadmill distance ("TMDist") [m] * 3D (XYZ) coordinates of 44 reflective markers #### File: WindingPaths\_Kinematics\_HIFW\_n24.mat **Description:** Kinematic (motion capture) marker data (100 Hz) for all walking trials performed by each participant on the High-Frequency Wide (HIFW) walking paths. ##### Variables * Video Frame # * Time [s] * Treadmill distance ("TMDist") [m] * 3D (XYZ) coordinates of 44 reflective markers #### File: WindingPaths\_Kinematics\_LOFN\_n24.mat **Description:** Kinematic (motion capture) marker data (100 Hz) for all walking trials performed by each participant on the Low-Frequency Narrow (LOFN) walking paths. ##### Variables * Video Frame # * Time [s] * Treadmill distance ("TMDist") [m] * 3D (XYZ) coordinates of 44 reflective markers #### File: WindingPaths\_Kinematics\_LOFW\_n24.mat **Description:** Kinematic (motion capture) marker data (100 Hz) for all walking trials performed by each participant on the Low-Frequency Wide (LOFW) walking paths. ##### Variables * Video Frame # * Time [s] * Treadmill distance ("TMDist") [m] * 3D (XYZ) coordinates of 44 reflective markers #### File: WindingPaths\_Kinematics\_STRN\_n24.mat **Description:** Kinematic (motion capture) marker data (100 Hz) for all walking trials performed by each participant on the Straight Narrow (STRN) walking paths. ##### Variables * Video Frame # * Time [s] * Treadmill distance ("TMDist") [m] * 3D (XYZ) coordinates of 44 reflective markers #### File: WindingPaths\_Kinematics\_STRW\_n24.mat **Description:** Kinematic (motion capture) marker data (100 Hz) for all walking trials performed by each participant on the Straight Wide (STRW) walking paths. ##### Variables * Video Frame # * Time [s] * Treadmill distance ("TMDist") [m] * 3D (XYZ) coordinates of 44 reflective markers ## Code/software Primary Data files are in Matlab *.mat format ([https://www.mathworks.com/](https://www.mathworks.com/)) There are multiple open-source alternatives to Matlab. Two common alternatives include GNU Octave Octave ([https://octave.org/](https://octave.org/)) and SciLab ([https://www.scilab.org/](https://www.scilab.org/)), but numerous others exist as well. Additional / ancillary data files (2) are in Microsoft Excel *.xlxs format ([https://www.microsoft.com/](https://www.microsoft.com/)). There are multiple open-source alternatives to Microsoft Excel.  The most prominent of these is probably LibreOffice ([https://libreoffice.org/](https://libreoffice.org/)), but other options can easily be found also. ## Access information Other publicly accessible locations of the data: * N/A Data was derived from the following sources: * N/A