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Rehabilitation Dataset Directory: Dataset Profile
Dataset: Stroke Initiative for Gait Data Evaluation (STRIDE)
|Dataset Full Name||Stroke Initiative for Gait Data Evaluation|
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.
Gait, Locomotion, Physical rehabilitation, Stroke, Treadmill, Walking, Gait, Motion capture
National Institutes of Health (NIH)
|Health Conditions/Disability Measures|
Berg balance score (BBS), Lower extremity Fugl-Meyer score (out of 34 possible points)
|Measures/Outcomes of Interest|
Walking stride, Gait, Treadmill, Motion capture, Kinematic (100 Hz) and kinetic (1000 Hz) data, three-dimensional motion capture, Visual3D, Metabolic cost of transport
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
|Unit of Observation||
Data collected at the following locations:
Data files broken down by location (see above)
|Data Collection Mode||
|Data Collection Frequency||
|Strengths and Limitations|
Extensive motion capture time series data available for each subject's step. Consistent "Core" of data collected across all 4 sites.
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 Access Requirements||
Data Use agreement, No cost
Example file structure for Subject 1 (S1) from Johns Hopkins University (JHU), 2019.
Separate folder for each subject
Stroke focused studies:
Johns Hopkins University data:
Persons post-stroke restore step length symmetry by walking asymmetrically
University of Pittsburgh data:
Self-selected step length asymmetry is not explained by energy cost minimization in individuals with chronic stroke.
Cerebral Contribution to the Execution, But Not Recalibration, of Motor Commands in a Novel Walking Environment
Augmenting propulsion demands during split-belt walking increases locomotor adaptation of asymmetric step lengths
University of Southern California data:
Using biofeedback to reduce spatiotemporal asymmetry impairs dynamic balance in people post-stroke
Individual Differences in Locomotor Function Predict the Capacity to Reduce Asymmetry
See "ReadMe" metadata text file (contained within each participating site's downloadable ZIP data file.)
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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).
Other CLDR supported resources and collaborative opportunities:
- Archive of Data on Disability to Enable Policy and research (ADDEP)
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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.
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