Deep learning classification of canine behavior using a single collar-mounted accelerometer: Real-world validation
| Autor: | Sophie Bradley, David E. Allen, Robert D. Chambers, Garrett Wymore, Laura M. Prescott, Nathanael C. Yoder, Aletha B. Carson, Scott Lyle, Kevin C K Lloyd, Christian Junge |
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| Rok vydání: | 2020 |
| Předmět: |
040301 veterinary sciences
Computer science Veterinary medicine activity monitor canine Accelerometer Machine learning computer.software_genre Article Collar 0403 veterinary science 03 medical and health sciences Sniffing SF600-1100 Accelerometer data 030304 developmental biology 0303 health sciences General Veterinary behavior business.industry Event (computing) Deep learning deep learning 04 agricultural and veterinary sciences accelerometer QL1-991 Eating behavior Animal Science and Zoology Artificial intelligence Licking business Zoology True positive rate computer |
| Zdroj: | Animals : an Open Access Journal from MDPI Animals Volume 11 Issue 6 Animals, Vol 11, Iss 1549, p 1549 (2021) |
| DOI: | 10.1101/2020.12.14.422660 |
| Popis: | Simple Summary Collar-mounted activity monitors using battery-powered accelerometers can continuously and accurately analyze specific canine behaviors and activity levels. These include normal behaviors and those that are indicators of disease conditions such as scratching, inappetence, excessive weight, or osteoarthritis. Algorithms used to analyze activity data are validated by video recordings of specific canine behaviors, which were used to label accelerometer data. The study described here was noteworthy for the large volume of data collected from more than 2500 dogs in clinical and real-world home settings. The accelerometer data were analyzed by a machine learning methodology, whereby algorithms were continually updated as additional data were acquired. The study determined that algorithms from the accelerometer data detected eating and drinking behaviors with a high degree of accuracy. Accurate detection of other behaviors such as licking, petting, rubbing, scratching, and sniffing was also demonstrated. The study confirmed that activity monitors using validated algorithms can accurately detect important health-related canine behaviors via a collar-mounted accelerometer. The validated algorithms have widespread practical benefits when used in commercially available canine activity monitors. 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 pet activity monitors. We built machine learning training databases from more than 5000 videos of more than 2500 dogs and ran the algorithms in production on more than 11 million days of device data. We then surveyed project participants representing 10,550 dogs, which provided 163,110 event responses to validate real-world detection of eating and drinking behavior. The resultant algorithm displayed a sensitivity and specificity for detecting drinking behavior (0.949 and 0.999, respectively) and eating behavior (0.988, 0.983). We also demonstrated detection of licking (0.772, 0.990), petting (0.305, 0.991), rubbing (0.729, 0.996), scratching (0.870, 0.997), and sniffing (0.610, 0.968). We show that the devices’ position on the collar had no measurable impact on performance. In production, users reported a true positive rate of 95.3% for eating (among 1514 users), and of 94.9% for drinking (among 1491 users). The study demonstrates the accurate detection of important health-related canine behaviors using a collar-mounted accelerometer. We trained and validated our algorithms on a large and realistic training dataset, and we assessed and confirmed accuracy in production via user validation. |
| Databáze: | OpenAIRE |
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