2016
DOI: 10.1242/jeb.142588
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Preferred gait and walk–run transition speeds in ostriches measured using GPS-IMU sensors

Abstract: The ostrich (Struthio camelus) is widely appreciated as a fast and agile bipedal athlete, and is a useful comparative bipedal model for human locomotion. Here, we used GPS-IMU sensors to measure naturally selected gait dynamics of ostriches roaming freely over a wide range of speeds in an open field and developed a quantitative method for distinguishing walking and running using accelerometry. We compared freely selected gait-speed distributions with previous laboratory measures of gait dynamics and energetics… Show more

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Cited by 38 publications
(41 citation statements)
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“…We performed this analysis on the data from Daley et al . 41 and found similar mean relative values of predicted and calculated walk-to-run transition stride frequencies for the ostriches. The values were 0.41 and 0.44, respectively.…”
Section: Discussionsupporting
confidence: 63%
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“…We performed this analysis on the data from Daley et al . 41 and found similar mean relative values of predicted and calculated walk-to-run transition stride frequencies for the ostriches. The values were 0.41 and 0.44, respectively.…”
Section: Discussionsupporting
confidence: 63%
“…Of note is that data on preferred gait as well as walk-to-run transition in ostriches were recently reported 41 . Thus, the authors reported mean values of freely chosen velocities during freely walking and running as well as the transition velocity.…”
Section: Discussionmentioning
confidence: 99%
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“…Unfortunately, information about T. Rex traces is very limited. Moreover, to use formula (7), we need the maximal length of the running stride, which is much larger and happens much less often in comparison with the strides corresponding to the comfortable running (see, e.g., the data about ostriches in [11]). We can use the information about the relative stride referred to the leg length.…”
Section: Estimations Of the Drag-to-weight Ratiomentioning
confidence: 99%
“…Such a system needs to be able to remove periods of non-steady locomotion and segment the data into strides. Previous studies trained animals to move at steady speeds and removed nonsteady locomotion by using a threshold based on speed (Maes et al, 2008) and velocity heading (Daley et al, 2016). Stride segmentation has been carried out using supervised machine learning (Mannini and Sabatini, 2012;Pfau et al, 2008).…”
Section: Introductionmentioning
confidence: 99%