Data from: A low-cost markerless motion capture system to automate functional gait assessment: Feasibility study
Data files
May 04, 2026 version files 10.13 MB
-
cha_psfmc.mat
3.54 MB
-
jer_psfmc.mat
3.02 MB
-
os_psfmc.mat
3.57 MB
-
README.md
2.90 KB
Abstract
Functional gait assessments in older adults have traditionally required manual in-person quantification of clinical measures such as walking speed and step placement. This reliance on individuals trained in motion analysis hinders the frequency with which they are performed, and reduces their generalizability and replicability. To standardize, simplify, and broaden access to gait assessments we here deploy recently-developed open-source tools to produce a low-cost, AI driven markerless motion capture system with custom analysis software for Functional Gait Assessment. Our system uses 3 Cameras, and was validated with a traditional marker-based system on subjects (N = 3), showing strong correlation to laboratory-standard measures of step length (R2 = 0.98), step width (R2 = 0.97), and head speed (R2 = 0.95). The markerless system’s FGA reports demonstrated data similar to previous FGA findings in older adult subjects (N = 5). Moreover, supplemental standard biomechanical gait measures Step Width, walking Duration, and continuous Gait Speed may be integrated to augment existing FGA. This study demonstrates a proof-of-principle open-source markerless system for analyses of functional gait.
Dataset DOI: 10.5061/dryad.2bvq83c2t
Description of the data and file structure
Jeremy D. Wong, William J. Herspiegel, and Arthur D. Kuo
Quickstart
After downloading files, clone the github repository kinarmdataanalysis, and run files prefixed with 'gofigures_psfmc_swsl' to regenerate figures testing step length and width hypothesis testing from the manuscript.
Files and variables
File: individual subject files for each subject in experiment 1.
File: jer_psfmc.mat
Description: phasespace-freemocap data:
Variables: 3D trajectories for the following
- fmc_bodies: (COCO (Common Objects in Context) Keypoints Dataset, 17 keypoints) for each of 6 trials (regular, wide): extracted gait only
- fmc_bodies: (COCO (Common Objects in Context) Keypoints Dataset, 17 keypoints) for each of 6 trials (regular, wide): entire trial
- ps_bodies: phasespace markers (labelled left foot, right_foot, left_heel, right_heel, left_toe, right_toe, head, hip)
- slenall_fmc, slenall_ps: step lengths for each
- swall_fmc, swall_ps: step widths for each
- tab: metadata for each trial.
File: os_psfmc.mat
Variables: 3D trajectories for the following
- fmc_bodies: (COCO (Common Objects in Context) Keypoints Dataset, 17 keypoints) for each of 6 trials (regular, wide): extracted gait only
- fmc_bodies: (COCO (Common Objects in Context) Keypoints Dataset, 17 keypoints) for each of 6 trials (regular, wide): entire trial
- ps_bodies: phasespace markers (labelled left foot, right_foot, left_heel, right_heel, left_toe, right_toe, head, hip)
- slenall_fmc, slenall_ps: step lengths for each
- swall_fmc, swall_ps: step widths for each
- tab: metadata for each trial.
File: cha_psfmc.mat
Variables: 3D trajectories for the following
- fmc_bodies: (COCO (Common Objects in Context) Keypoints Dataset, 17 keypoints) for each of 6 trials (regular, wide): extracted gait only
- fmc_bodies: (COCO (Common Objects in Context) Keypoints Dataset, 17 keypoints) for each of 6 trials (regular, wide): entire trial
- ps_bodies: phasespace markers (labelled left foot, right_foot, left_heel, right_heel, left_toe, right_toe, head, hip)
- slenall_fmc, slenall_ps: step lengths for each
- swall_fmc, swall_ps: step widths for each
- tab: metadata for each trial.
Code/software
git repository to clone for data analysis: https://bitbucket.org/jdwongmcl/kinarmdataanalysis/src/master/
Human subjects data
I received explicit consent for de-identified data in the public domain. We used randomized unique string identifiers for each subject.
