ArticleFrontiers in veterinary science2022
Measurement of Canine Ataxic Gait Patterns Using Body-Worn Smartphone Sensor Data.
Article in Frontiers in veterinary science, 2022. 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.
- Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species Technology Transfer.Veterinary medicine and science · 2026Review
- Establishing Operational Descriptive Definitions for Neurologic Abnormalities Identified During Gaiting in Dogs.Animals : an open access journal from MDPI · 2026Review
- Canine gait analysis using inertial sensors and deep learning for orthopedic and neurological disorders.Scientific reports · 2026Article
- One digital health through wearables: a viewpoint on human-pet integration towards Healthcare 5.0.Frontiers in digital health · 2025Article
- Automation in canine science: enhancing human capabilities and overcoming adoption barriers.Frontiers in veterinary science · 2024Article
- Quantification of phenobarbital-induced ataxia in dogs with idiopathic epilepsy.Frontiers in veterinary science · 2023Article
- Quantification of spinal ataxia in dogs with thoracolumbar spinal cord injury.Frontiers in veterinary science · 2023Article
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Authors and funding
7 authors.
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Abstract
Ataxia is an impairment of the coordination of movement or the interaction of associated muscles, accompanied by a disturbance of the gait pattern. Diagnosis of this clinical sign, and evaluation of its severity is usually done using subjective scales during neurological examination. In this exploratory study we investigated if inertial sensors in a smart phone (3 axes of accelerometer and 3 axes of gyroscope) can be used to detect ataxia. The setting involved inertial sensor data collected by smartphone placed on the dog's back while walking in a straight line. A total of 770 walking sessions were evaluated comparing the gait of 55 healthy dogs to the one of 23 dogs with ataxia. Different machine learning techniques were used with the K-nearest neighbors technique reaching 95% accuracy in discriminating between a healthy control group and ataxic dogs, indicating potential use for smartphone apps for canine ataxia diagnosis and monitoring of treatment effect.
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