Longitudinal quasi-static stability predicts changes in dog gait on rough terrain

• Published in: Journal of Experimental Biology
• Main Authors: Wilshin, Simon, Reeve, Michelle A., Haynes, G. Clark, Revzen, Shai, Koditschek, Daniel E., Spence, Andrew J.
• Format: Text
• Language: English
• Published: The Company of Biologists Ltd 2017
• Subjects: Research Article; Canis lupus
• Online Access: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5450805/; http://www.ncbi.nlm.nih.gov/pubmed/28264903; https://doi.org/10.1242/jeb.149112

Legged animals utilize gait selection to move effectively and must recover from environmental perturbations. We show that on rough terrain, domestic dogs, Canis lupus familiaris, spend more time in longitudinal quasi-statically stable patterns of movement. Here, longitudinal refers to the rostro-caudal axis. We used an existing model in the literature to quantify the longitudinal quasi-static stability of gaits neighbouring the walk, and found that trot-like gaits are more stable. We thus hypothesized that when perturbed, the rate of return to a stable gait would depend on the direction of perturbation, such that perturbations towards less quasi-statically stable patterns of movement would be more rapid than those towards more stable patterns of movement. The net result of this would be greater time spent in longitudinally quasi-statically stable patterns of movement. Limb movement patterns in which diagonal limbs were more synchronized (those more like a trot) have higher longitudinal quasi-static stability. We therefore predicted that as dogs explored possible limb configurations on rough terrain at walking speeds, the walk would shift towards trot. We gathered experimental data quantifying dog gait when perturbed by rough terrain and confirmed this prediction using GPS and inertial sensors (n=6, P<0.05). By formulating gaits as trajectories on the n-torus we are able to make tractable the analysis of gait similarity. These methods can be applied in a comparative study of gait control which will inform the ultimate role of the constraints and costs impacting locomotion, and have applications in diagnostic procedures for gait abnormalities, and in the development of agile legged robots.