ArticlePloS one2018
Automatic characterization of stride parameters in canines with a single wearable inertial sensor.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Canine gait analysis using inertial sensors and deep learning for orthopedic and neurological disorders.Scientific reports · 2026Article
- Inertial measurement unit-based characterization of gait patterns in Asian elephants: clinically sound elephants and elephants with gait abnormalities.Frontiers in veterinary science · 2026Article
- Evaluating Gait Abnormalities in Asian Elephants Using Inertial Measurement Unit-Based Vertical Movement Symmetry Analysis: A Pilot Study.Veterinary sciences · 2025Article
- Forelimb muscle activity during level and progressive incline and decline walking in dogs and implications for rehabilitation.Frontiers in veterinary science · 2025Article
- Four-limb wireless IMU sensor system for automatic gait detection in canines.Scientific reports · 2022Article
- Analysis of Agile Canine Gait Characteristics Using Accelerometry.Sensors (Basel, Switzerland) · 2019Article
- Questions Answered and Unanswered by the First CRISPR Editing Study in a Canine Model of Duchenne Muscular Dystrophy.Human gene therapy · 2019Article
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Authors and funding
5 authors.
Funding
Abstract
BACKGROUND AND
objectiveGait analysis is valuable for studying neuromuscular and skeletal diseases. Wearable motion sensors or inertial measurement units (IMUs) have become common for human gait analysis. Canines are important large animal models for translational research of human diseases. Our objective is to develop a method for accurate and reliable determination of the timing of each stride in dogs using a wearable IMU.
methodsWe built a wireless IMU sensor using off-the-shelf components. We also developed a MATLAB algorithm for data acquisition and stride timing determination. Stride parameters from 1,259 steps of three adult mixed breed dogs were determined across a range of six height-normalized speeds using the IMU system. The IMU results were validated by frame-by-frame manual counting of high-speed video recordings.
resultsComparing IMU derived results with video revealed that the mean error ± standard deviation for stride, stance, and swing duration was 0.001 ± 0.025, -0.001 ± 0.030, and 0.001 ± 0.019 s respectively. A mean error ± standard deviation of 0.000 ± 0.020 and -0.008 ± 0.027 s was obtained for determining toe-off and toe-touch events respectively. Only one step was missed by the algorithm in the video dataset of 1,259 steps.
conclusionWe have developed and validated an IMU method for automatic canine gait analysis. Our method can be used for studying neuromuscular diseases in veterinary clinics and in translational research.
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