2021
DOI: 10.3390/ani11061549
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Deep Learning Classification of Canine Behavior Using a Single Collar-Mounted Accelerometer: Real-World Validation

Abstract: Collar-mounted canine activity monitors can use accelerometer data to estimate dog activity levels, step counts, and distance traveled. With recent advances in machine learning and embedded computing, much more nuanced and accurate behavior classification has become possible, giving these affordable consumer devices the potential to improve the efficiency and effectiveness of pet healthcare. Here, we describe a novel deep learning algorithm that classifies dog behavior at sub-second resolution using commercial… Show more

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Cited by 43 publications
(42 citation statements)
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“…Small devices such as accelerometers are also used in the pet industry. They commonly attach to collars and accurately capture location and movement and are being developed further using machine learning to accurately detect other behaviors [ 69 ]. Recent advances in pet technology mean that these types of devices, along with other types of wearables (harnesses, etc.…”
Section: Discussionmentioning
confidence: 99%
“…Small devices such as accelerometers are also used in the pet industry. They commonly attach to collars and accurately capture location and movement and are being developed further using machine learning to accurately detect other behaviors [ 69 ]. Recent advances in pet technology mean that these types of devices, along with other types of wearables (harnesses, etc.…”
Section: Discussionmentioning
confidence: 99%
“…We elected to use an IMU box on the ventral part of the neck of the dog as a point of reference because it is a common place for activity monitors to be placed in clinical trials [ 20 – 23 ]. Inertial measurement units are motion-based sensors that offer an opportunity to monitor the activity of a canine patient in their natural environment; information that would be useful for determining the impact of disease burden and treatments for osteoarthritis, chronic pain, cardiovascular disease, and obesity.…”
Section: Discussionmentioning
confidence: 99%
“…These results helped the current work to decide the optimal sensor placement, which was mounting a harness on the back of the SaR dog with the developed device in it. Finally, the authors in [29] created a huge dataset exploiting a 3-axial accelerometer and collecting data from more than 2500 dogs of multiple breeds. Then they trained a deep learning classifier which was then validated for a real-world detection of eating and drinking behavior.…”
Section: Activity Recognitionmentioning
confidence: 99%