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Wilson C, Ebbecke D, Berger D, Otto C. The Effects of Fitness Training on Working Dog Behavior: Two Case Studies. Vet Clin North Am Small Anim Pract 2024; 54:87-99. [PMID: 37722948 DOI: 10.1016/j.cvsm.2023.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/20/2023]
Abstract
Working dogs perform complex tasks that require both physical and behavioral soundness. Two case studies demonstrate how fitness training moderated arousal levels, facilitated training, and improved performance measures in different situations. Fitness training can be beneficial when integrated as part of a working dog's training regimen because it can have a significant influence on their overall health, behavior, and ability to perform their working role effectively.
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Affiliation(s)
- Clara Wilson
- The Penn Vet Working Dog Center, 3401 Grays Ferry Avenue, Philadelphia, PA 19146, USA.
| | - Dana Ebbecke
- The Penn Vet Working Dog Center, 3401 Grays Ferry Avenue, Philadelphia, PA 19146, USA
| | - Danielle Berger
- The Penn Vet Working Dog Center, 3401 Grays Ferry Avenue, Philadelphia, PA 19146, USA
| | - Cynthia Otto
- The Penn Vet Working Dog Center, 3401 Grays Ferry Avenue, Philadelphia, PA 19146, USA
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Eyre AW, Zapata I, Hare E, Serpell JA, Otto CM, Alvarez CE. Machine learning prediction and classification of behavioral selection in a canine olfactory detection program. Sci Rep 2023; 13:12489. [PMID: 37528118 PMCID: PMC10394074 DOI: 10.1038/s41598-023-39112-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 07/20/2023] [Indexed: 08/03/2023] Open
Abstract
There is growing interest in canine behavioral research specifically for working dogs. Here we take advantage of a dataset of a Transportation Safety Administration olfactory detection cohort of 628 Labrador Retrievers to perform Machine Learning (ML) prediction and classification studies of behavioral traits and environmental effects. Data were available for four time points over a 12 month foster period after which dogs were accepted into a training program or eliminated. Three supervised ML algorithms had robust performance in correctly predicting which dogs would be accepted into the training program, but poor performance in distinguishing those that were eliminated (~ 25% of the cohort). The 12 month testing time point yielded the best ability to distinguish accepted and eliminated dogs (AUC = 0.68). Classification studies using Principal Components Analysis and Recursive Feature Elimination using Cross-Validation revealed the importance of olfaction and possession-related traits for an airport terminal search and retrieve test, and possession, confidence, and initiative traits for an environmental test. Our findings suggest which tests, environments, behavioral traits, and time course are most important for olfactory detection dog selection. We discuss how this approach can guide further research that encompasses cognitive and emotional, and social and environmental effects.
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Affiliation(s)
- Alexander W Eyre
- Center for Clinical and Translational Research, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, 43205, USA
| | - Isain Zapata
- Department of Biomedical Sciences, Rocky Vista University College of Osteopathic Medicine, Parker, CO, 80134, USA
| | - Elizabeth Hare
- Dog Genetics LLC, Astoria, NY, 11102, USA
- Penn Vet Working Dog Center, Department of Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, 19146, USA
| | - James A Serpell
- Department of Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Cynthia M Otto
- Penn Vet Working Dog Center, Department of Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, 19146, USA
| | - Carlos E Alvarez
- Departments of Pediatrics and Veterinary Clinical Sciences, The Ohio State University Colleges of Medicine and Veterinary Medicine, Columbus, OH, 43210, USA.
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Ramos MT, Farr BD, Otto CM. Rehabilitation to Return-to-Work for Working Dogs. Vet Clin North Am Small Anim Pract 2023; 53:869-878. [PMID: 36964026 DOI: 10.1016/j.cvsm.2023.02.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2023]
Abstract
This article highlights the differences between working dog careers, unique protocols associated with health care of a working dog and provides a practical guide to creating and managing a return-to-work program. The rehabilitative approach to a working dog consists of four distinct sequential phases: activity restriction, rehabilitation, return-to-work, and maintenance. The timeline through each phase is dependent on the degree of injury, treatment intervention, prior health status of the dog, and compliance of the handler. Return-to-work for a working dog is considered a success if the dog can perform all career-related activities safely and proficiently.
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Affiliation(s)
- Meghan T Ramos
- Penn Vet Working Dog Center, Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, 3401 Grays Ferry Avenue, Philadelphia, PA 19146, USA.
| | - Brian D Farr
- Department of Defense Military Working Dog Veterinary Service, Joint Base San Antonio - Lackland Air Force Base, San Antonio, TX, USA
| | - Cynthia M Otto
- Penn Vet Working Dog Center, Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, 3401 Grays Ferry Avenue, Philadelphia, PA 19146, USA
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Byrne C, Starner T, Jackson M. Quantifying canine interactions with smart toys assesses suitability for service dog work. Front Vet Sci 2022; 9:886941. [PMID: 36118349 PMCID: PMC9481248 DOI: 10.3389/fvets.2022.886941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 07/22/2022] [Indexed: 11/13/2022] Open
Abstract
There are approximately a half million active service dogs in the United States, providing life-changing assistance and independence to people with a wide range of disabilities. The tremendous value of service dogs creates significant demand, which service dog providers struggle to meet. Breeding, raising, and training service dogs is an expensive, time-consuming endeavor which is exacerbated by expending resources on dogs who ultimately will prove to be unsuitable for service dog work because of temperament issues. Quantifying behavior and temperament through sensor-instrumented dog toys can provide a way to predict which dogs will be suitable for service dog work, allowing resources to be focused on the dogs likely to succeed. In a 2-year study, we tested dogs in advanced training at Canine Companions for Independence with instrumented toys, and we discovered that a measure of average bite duration is significantly correlated with a dog's placement success as a service dog [Adjusted OR = 0.12, Pr(>|z|) = 0.00666]. Applying instrumented toy interactions to current behavioral assessments could yield more accurate measures for predicting successful placement of service dogs while reducing the workload of the trainers.
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Affiliation(s)
- Ceara Byrne
- Traverso Lab, Massachusetts Institute of Technology, Boston, MA, United States
- *Correspondence: Ceara Byrne
| | - Thad Starner
- Animal Centered Computing Lab, Georgia Institution of Technology, Atlanta, GA, United States
| | - Melody Jackson
- Animal Centered Computing Lab, Georgia Institution of Technology, Atlanta, GA, United States
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