2021
DOI: 10.1016/j.jbiomech.2020.110146
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Reliable and clinically applicable gait event classification using upper body motion in walking and trotting horses

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Cited by 22 publications
(24 citation statements)
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“…Stride speed was calculated by smoothed differentiation of the horizontal coordinates (x, y) of the reflective marker between the tubera sacrale. Gait events (hindlimb impact events) were detected in accordance with the method described by Roepstorff et al ( 48 ). These events were manually imported into Visual3D for stride segmentation of sEMG and kinematic data.…”
Section: Methodsmentioning
confidence: 99%
“…Stride speed was calculated by smoothed differentiation of the horizontal coordinates (x, y) of the reflective marker between the tubera sacrale. Gait events (hindlimb impact events) were detected in accordance with the method described by Roepstorff et al ( 48 ). These events were manually imported into Visual3D for stride segmentation of sEMG and kinematic data.…”
Section: Methodsmentioning
confidence: 99%
“…Kinematic data were analysed using custom‐made Matlab scripts (Matlab, The MathWorks, Inc.). Filtering (Butterworth high pass filter, cut‐off 70% of stride frequency) 20 and stride segmentation 21 were performed as described previously. Single strides were excluded if stride duration or tuber sacrale vertical range of motion (ROM) differed more than 20%, or if head vertical ROM differed more than 40% from measurement median.…”
Section: Methodsmentioning
confidence: 99%
“…Kinematic data were analysed using custom-written Matlab scripts. Stride segmentation was done based on the vertical maxima for the tubera sacrale marker, and pelvis roll to determine left vs right hind limb stance 29 . For this purpose, data were filtered using a zero-lag Butterworth high-pass filter with a cut-off frequency of 70% of the stride frequency 30 .…”
Section: Methodsmentioning
confidence: 99%