{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T03:34:31Z","timestamp":1792553671884,"version":"4.5.0"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T00:00:00Z","timestamp":1659571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Vet. Sci."],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3389\/fvets.2022.912253","type":"journal-article","created":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T00:43:55Z","timestamp":1659573835000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Measurement of Canine Ataxic Gait Patterns Using Body-Worn Smartphone Sensor 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