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Rehabilitation Dataset Directory: Dataset Profile

Dataset: Stroke Initiative for Gait Data Evaluation (STRIDE)

Basic Information
Dataset Full Name Stroke Initiative for Gait Data Evaluation
Dataset Acronym STRIDE
Summary

The Stroke Initiative for Gait Data Evaluation (STRIDE) is an initiative based at the University of Southern California to create an inter-institutional, public database containing de-identified demographic and kinematic, kinetic, and spatiotemporal measures assessed via gait analysis in individuals post-stroke, to provide a larger and more heterogeneous research dataset than that typically amassed at a single institution. The purpose of the study was to understand how persons post-stroke generate symmetric steps and how the resulting gait pattern relates to the metabolic cost of transport. Data was collected at the following institutions: Johns Hopkins University, University of Southern California, University of Pittsburgh and Emory University.


55 persons post-stroke walked on an instrumented treadmill under two conditions: preferred walking and symmetric stepping (using visual feedback). Kinematic and kinetic data was collected during both conditions via three-dimensional motion capture, as well as metabolic data.


The data in STRIDE can be used to run pilot analyses and power calculations for research studies, design and validate statistical models to test associations between gait variables, provides data for simulation-based biomechanical studies in stroke, and provides data to assess the reproducibility of research findings.
Key Terms

Gait, Locomotion, Physical rehabilitation, Stroke, Treadmill, Walking, Gait, Motion capture
Study Design Cross-Sectional
Data Type(s) Clinical
Sponsoring Agency/Entity

National Institutes of Health (NIH)
Health Conditions/Disability Measures
Health Condition(s)

Stroke
Disability Measures

Ambulatory disability

Berg balance score (BBS), Lower extremity Fugl-Meyer score (out of 34 possible points)
Measures/Outcomes of Interest
Topics

Walking stride, Gait, Treadmill, Motion capture, Kinematic (100 Hz) and kinetic (1000 Hz) data, three-dimensional motion capture, Visual3D, Metabolic cost of transport
Sample
Sample Population

Survivors of a first, unilateral stroke between 18 and 90 years of age across the United States who are able to walk on a treadmill for more than 2 minutes
Sample Size/Notes

55 persons 
Unit of Observation

Individual/Patient
Continent(s)

North America
Countries

United States
Geographic Coverage
Data collected at the following locations:
  • Johns Hopkins University, Baltimore MD
  • University of Southern California
  • University of Pittsburgh, PA
  • Emory University, Atlanta, GA
Geographic Specificity

Data files broken down by location (see above)
Data Collection
Data Collection Mode

Clinical/experimental
Years Collected

2012-20
Data Collection Frequency

NA
Strengths and Limitations
Strengths

Extensive motion capture time series data available for each subject's step. Consistent "Core" of data collected across all 4 sites.
Limitations

Limited documentation. Variation in content by location - some include additional data files that others don't have. Single participant data are presented in individual folders. Each participant's data broken into separate limb measures - up to 15 datafiles per individual.
Data Details
Primary Website

https://www.icpsr.umich.edu/web/ICPSR/studies/38002# 
Data Access

https://www.icpsr.umich.edu/web/ICPSR/studies/38002# 
Data Access Requirements

Data Use agreement, No cost
Summary Tables/Reports

NA
Data Components

Example file structure for Subject 1 (S1) from Johns Hopkins University (JHU), 2019.

Separate folder for each subject

  • Subject demographics, scores etc.
    • JHU_2019_s1_Demo.csv
  • Ground reaction forces (GRF) timeseries in Newtons, for paretic (P) or non-paretic (NP) extremity
    • JHU_2019_s1_vert_GRF_NP.csv         Vert (vertical direction) 
    • JHU_2019_s1_vert_GRF_P.csv
    • JHU_2019_s1_AP_GRF_NP.csv         Fore-aft direction
    • JHU_2019_s1_AP_GRF_P.csv
    • JHU_2019_s1_ML_GRF_NP.csv         ML (medio-lateral direction)
    • JHU_2019_s1_ML_GRF_P.csv
  • Stance and swing times in seconds (each row is a different stride)
    • JHU_2019_s1_StanceTime_NP.csv Stance  times 
    • JHU_2019_s1_StanceTime_P.csv
    • JHU_2019_s1_SwingTime_NP.csv         Swing times
    • JHU_2019_s1_SwingTime_P.csv
  • Step lengths in meters (each row is a different stride)
    • JHU_2019_s1_StepLength_NP.csv
    • JHU_2019_s1_StepLength_P.csv
Similar/Related Dataset(s)
Stroke focused studies:
Selected Papers
Other Papers

Johns Hopkins University data:

Persons post-stroke restore step length symmetry by walking asymmetrically

https://www.biorxiv.org/content/10.1101/799775 


University of Pittsburgh data:

Self-selected step length asymmetry is not explained by energy cost minimization in individuals with chronic stroke.

https://pubmed.ncbi.nlm.nih.gov/32847596/ 

Cerebral Contribution to the Execution, But Not Recalibration, of Motor Commands in a Novel Walking Environment

https://pubmed.ncbi.nlm.nih.gov/32001549/ 

Augmenting propulsion demands during split-belt walking increases locomotor adaptation of asymmetric step lengths

https://pubmed.ncbi.nlm.nih.gov/32493440/ 


University of Southern California data:

Using biofeedback to reduce spatiotemporal asymmetry impairs dynamic balance in people post-stroke

https://journals.sagepub.com/doi/abs/10.1177/15459683211019346 

Individual Differences in Locomotor Function Predict the Capacity to Reduce Asymmetry

https://journals.sagepub.com/doi/10.1177/1545968318787913 
Technical

See "ReadMe" metadata text file (contained within each participating site's downloadable ZIP data file.)
Related Repositories
Repositories

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The Rehabilitation Research Cross-dataset Variable Catalog has been developed through the Center for Large Data Research & Data Sharing in Rehabilitation (CLDR). The Center for Large Data Research and Data Sharing in Rehabilitation involves a consortium of investigators from the University of Texas Medical Branch, Cornell University's Yang Tan Institute (YTI), and the University of Michigan. The CLDR is funded by NIH - National Institute of Child Health and Human Development, through the National Center for Medical Rehabilitation Research, the National Institute for Neurological Disorders and Stroke, and the National Institute of Biomedical Imaging and Bioengineering. (P2CHD065702).

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Acknowledgements: This tool was developed through the efforts of William Erickson and Arun Karpur, and web designers Jason Criss and Jeff Trondsen at Cornell University. Many thanks to graduate students Kyoung Jo Oh and Yeong Joon Yoon who developed much of the content used in this tool.

For questions or comments please contact disabilitystatistics@cornell.edu



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