Effect of Bar Jump Height on Kinetics and Kinematics of Take-off in Agility Dogs
PLoS ONE(2025)
Abstract
Sport-related injuries have been reported to occur in around one-third of agility dogs. Higher bar height in competitions has been shown to increase odds of an injury. This study evaluated the effect of bar height on the kinetics and kinematics at take-off to a bar jump. Forces from fore- and hindlimb pairs were measured with force plates. A three-dimensional motion capture system was used to measure sagittal joint kinematics of the shoulder, elbow, carpus, hip, stifle, and tarsal joints, as well as limb coordination, trunk horizontal velocity, take-off distance, and take-off angle. Data were collected for 17 Border Collies at three different bar heights: 80%, 100%, and 120% of wither height. A linear mixed model was used for statistical analysis. At higher bar height, decelerative impulses were greater and accelerative impulses decreased along with greater vertical impulses from forelimb and hindlimb pairs (p<0.001). Post-hoc analyses revealed differences between all three bar heights (p<0.01), except for forelimb decelerative impulse, which did not differ between 80% and 100% heights. Sagittal range of motion was greater, through increased peak flexion or extension, at 120% bar height than at lower bar heights (p<0.05) in almost all measured limb joints. The only exceptions were leading forelimb shoulder and elbow joints and leading hindlimb hip joint. With increasing bar height, the horizontal velocity of trunk decreased (p<0.001), and take-off angle became steeper (p<0.001), with all bar heights differing from each other (p<0.01). Temporal synchronicity between trailing and leading limbs increased and craniocaudal distance decreased in forelimbs (p<0.05) and hindlimbs (p<0.01) as bar height increased. Increased vertical and decelerative impulses, as well as the greater peak flexion and extension angles of joints, may indicate greater load on the tissues at higher bar heights, which could explain the increased odds of injury at higher bar heights in agility dogs.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined