ArticleAnimals : an open access journal from MDPI2021
Deep Learning Classification of Canine Behavior Using a Single Collar-Mounted Accelerometer: Real-World Validation.
Article in Animals : an open access journal from MDPI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Predicting missed alerts in detection dogs.Scientific reports · 2026Article
- TP-CanineNet: Temporal Context Contrastive Learning with Pseudo-Label Supervision for Abnormal Behavior Detection of Canine.Animals : an open access journal from MDPI · 2026Article
- A Novel Multidisciplinary Approach for Reptile Movement and Behavior Analysis.Integrative zoology · 2026Article
- The Expanding Role of Artificial Intelligence in Companion Animal Care: A Systematic Review.Animals : an open access journal from MDPI · 2026Review
- Continuous Activity Monitoring Using a Wearable Sensor in Dogs with Osteoarthritis: An Exploratory Case Series.Animals : an open access journal from MDPI · 2025Article
- Physical Activity Monitors in Companion Animal Chronic Pain Research-A Review Focused on Osteoarthritis Pain.Animals : an open access journal from MDPI · 2025Review
- An Indole-Rich Postbiotic Reduces Itching in Dogs: A Randomized, Double-Blinded Placebo-Controlled Study.Animals : an open access journal from MDPI · 2025Article
- Collagen Hydrolysates as Nutritional Support in Canine Osteoarthritis: A Narrative Review.Journal of animal physiology and animal nutrition · 2025Review
- One digital health through wearables: a viewpoint on human-pet integration towards Healthcare 5.0.Frontiers in digital health · 2025Article
- Automated Pipeline for Robust Cat Activity Detection Based on Deep Learning and Wearable Sensor Data.Sensors (Basel, Switzerland) · 2024Article
- A standardised approach to quantifying activity in domestic dogs.Royal Society open science · 2024Article
- Walk or be walked by the dog? The attachment role.BMC public health · 2024Article
- Comparative analysis of chronic neuropathic pain and pain assessment in companion animals and humans.Frontiers in veterinary science · 2024Review
- Machine learning prediction and classification of behavioral selection in a canine olfactory detection program.Scientific reports · 2023Article
- Margined Horn-Shaped Air Chamber for Body-Conduction Microphone.Sensors (Basel, Switzerland) · 2023Article
- TN-GAN-Based Pet Behavior Prediction through Multiple-Dimension Time-Series Augmentation.Sensors (Basel, Switzerland) · 2023Article
- Behavior-Based Video Summarization System for Dog Health and Welfare Monitoring.Sensors (Basel, Switzerland) · 2023Article
- Response of pet owners to Whistle FITFrontiers in veterinary science · 2023Article
- Automatic gross tumor segmentation of canine head and neck cancer using deep learning and cross-species transfer learning.Frontiers in veterinary science · 2023Article
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Authors and funding
10 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.