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A Canine Gait Analysis Protocol for Back Movement Assessment in German Shepherd Dogs
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10.3390:vetsci7010026.pdf
Date
2020-03-01
Author
Sürer, Elif
Cereatti, Andrea
Evangelisti, Maria Antonietta
Paolini, Gabriele
Della Croce, Ugo
Manunta, Maria Lucia
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Objective-To design and test a motion analysis protocol for the gait analysis of adult German Shepherd (GS) dogs with a focus in the analyses of their back movements. Animals-Eight clinically healthy adult large-sized GS dogs (age, 4 +/- 1.3 years; weight, 38.8 +/- 4.2 kg). Procedures-A six-camera stereo-photogrammetric system and two force platforms were used for data acquisition. Experimental acquisition sessions consisted of static and gait trials. During gait trials, each dog walked along a 6 m long walkway at self-selected speed and a total of six gait cycles were recorded. Results-Grand mean and standard deviation of ground reaction forces of fore and hind limbs are reported. Spatial-temporal parameters averaged over gait cycles and subjects, their mean, standard deviation and coefficient of variance are analyzed. Joint kinematics for the hip, stifle and tarsal joints and their average range of motion (ROM) values, and their 95% Confidence Interval (CI) values of kinematics curves are reported. Conclusions and Clinical Relevance-This study provides normative data of healthy GS dogs to form a preliminary basis in the analysis of the spatial-temporal parameters, kinematics and kinetics during quadrupedal stance posture and gait. Also, a new back movement protocol enabling a multi-segment back model is provided. Results show that the proposed gait analysis protocol may become a useful and objective tool for the evaluation of canine treatment with special focus on the back movement.
Subject Keywords
Gait analysis
,
German shepherd dogs
,
Back movement
,
Kinematics
,
Kinetics
,
Normative data
URI
https://hdl.handle.net/11511/32038
Journal
VETERINARY SCIENCES
DOI
https://doi.org/10.3390/vetsci7010026
Collections
Graduate School of Informatics, Article