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Automated Gait Analysis Through Hues and Areas (AGATHA): A Method to Characterize the Spatiotemporal Pattern of Rat Gait

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Abstract

While rodent gait analysis can quantify the behavioral consequences of disease, significant methodological differences exist between analysis platforms and little validation has been performed to understand or mitigate these sources of variance. By providing the algorithms used to quantify gait, open-source gait analysis software can be validated and used to explore methodological differences. Our group is introducing, for the first time, a fully-automated, open-source method for the characterization of rodent spatiotemporal gait patterns, termed Automated Gait Analysis Through Hues and Areas (AGATHA). This study describes how AGATHA identifies gait events, validates AGATHA relative to manual digitization methods, and utilizes AGATHA to detect gait compensations in orthopaedic and spinal cord injury models. To validate AGATHA against manual digitization, results from videos of rodent gait, recorded at 1000 frames per second (fps), were compared. To assess one common source of variance (the effects of video frame rate), these 1000 fps videos were re-sampled to mimic several lower fps and compared again. While spatial variables were indistinguishable between AGATHA and manual digitization, low video frame rates resulted in temporal errors for both methods. At frame rates over 125 fps, AGATHA achieved a comparable accuracy and precision to manual digitization for all gait variables. Moreover, AGATHA detected unique gait changes in each injury model. These data demonstrate AGATHA is an accurate and precise platform for the analysis of rodent spatiotemporal gait patterns.

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Acknowledgments

Research reported in this publication was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) of the National Institutes of Health under Award Numbers K99/R00AR057426 (Allen) and R01NS080180-01A1 (Fuller). The initial algorithms and concepts for AGATHA were developed by Dr. Kyle D. Allen during his time at Duke University (under support of F32AR056190). These initial algorithms and concepts could not have been constructed without the contributions from Mr. Ian King and Ms. Ashley Holmstrom, and their contributions to the initiation of this project are greatly appreciated.

Author Contribution

KDA conceptualized the AGATHA algorithms, which were implemented and troubleshot by HEK and TP. HEK and KDA designed and conducted the studies to evaluate AGATHA’s accuracy and precision and the study to evaluate AGATHA in an orthopaedic injury model. ST KS, EG-R, and DF designed the spinal cord injury studies, with gait data collected and analyzed by HEK. HEK and KDA conducted the statistical models to evaluate the data sets. Finally, HEK and KDA drafted the manuscript with all authors providing edits and commentary where needed. All authors have read and approved the final version of the manuscript.

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Authors and Affiliations

  1. J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, 1275 Center Drive, Biomedical Sciences Building, JG56, Gainesville, FL, 32610, USA

    Heidi E. Kloefkorn, Travis R. Pettengill & Kyle D. Allen

  2. Department of Physical Therapy, University of Florida, Gainesville, FL, USA

    Sara M. F. Turner, Kristi A. Streeter, Elisa J. Gonzalez-Rothi & David D. Fuller

Authors
  1. Heidi E. Kloefkorn
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  2. Travis R. Pettengill
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  3. Sara M. F. Turner
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  4. Kristi A. Streeter
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  5. Elisa J. Gonzalez-Rothi
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  6. David D. Fuller
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  7. Kyle D. Allen
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Correspondence to Kyle D. Allen.

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Associate Editor Joel D. Stitzel oversaw the review of this article.

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Kloefkorn, H.E., Pettengill, T.R., Turner, S.M.F. et al. Automated Gait Analysis Through Hues and Areas (AGATHA): A Method to Characterize the Spatiotemporal Pattern of Rat Gait. Ann Biomed Eng 45, 711–725 (2017). https://doi.org/10.1007/s10439-016-1717-0

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