Log my dog:Perceived impact of dog activity tracking

The pet industry is catching up in the wearables market, and pet activity and location trackers are increasingly worn by our furry friends. We report on an empirical study investigating the user perceptions of a popular dog activity tracker. Results show that these trackers have a positive impact on owners' motivation to increase their physical activities with their dogs

Effects of anxiety on canine movement in dog-robot interactions.

Canine behavioral disorders, such as various forms of fear and anxiety, are a major threat for the well-being of dogs and their owners. They are also the main cause for dog abandonment and relinquishment to shelters. Timely diagnosis and treatment of such problems is a complex task, requiring extensive behavioral expertise. Accurate classification of pathological behavior requires information on the dog's reactions to environmental stimuli. Such information is typically self-reported by the animal's owner, posing a threat to its accuracy and correctness. Simple robots have been used as controllable stimuli for evoking particular canine behaviors, leading to the increasing interest in dog-robot interactions (DRIs). We explore the use of DRIs as a tool for the assessment of canine behavioral disorders. More concretely, we ask in what ways disorders such as anxiety may be reflected in the way dogs interact with a robot. To this end, we performed an exploratory study, recording DRIs for a group of 20 dogs, consisting of 10 dogs diagnosed by a behavioral expert veterinarian with deprivation syndrome, a form of phobia/anxiety caused by inadequate development conditions, and 10 healthy control dogs. Pathological dogs moved significantly less than the control group during these interactions

Objective Video-Based Assessment of ADHD-Like Canine Behavior Using Machine Learning

Canine ADHD-like behavior is a behavioral problem that often compromises dogs’ well-being, as well as the quality of life of their owners; early diagnosis and clinical intervention are often critical for successful treatment, which usually involves medication and/or behavioral modification. Diagnosis mainly relies on owner reports and some assessment scales, which are subject to subjectivity. This study is the first to propose an objective method for automated assessment of ADHD-like behavior based on video taken in a consultation room. We trained a machine learning classifier to differentiate between dogs clinically treated in the context of ADHD-like behavior and health control group with 81% accuracy; we then used its output to score the degree of exhibited ADHD-like behavior. In a preliminary evaluation in clinical context, in 8 out of 11 patients receiving medical treatment to treat excessive ADHD-like behavior, H-score was reduced. We further discuss the potential applications of the provided artifacts in clinical settings, based on feedback on H-score received from a focus group of four behavior experts

Computational Analysis of Movement Patterns of Dogs with ADHD-Like Behavior

Computational approaches were called for to address the challenges of more objective behavior assessment which would be less reliant on owner reports. This study aims to use computational analysis for investigating a hypothesis that dogs with ADHD-like (attention deficit hyperactivity disorder) behavior exhibit characteristic movement patterns directly observable during veterinary consultation. Behavioral consultations of 12 dogs medically treated due to ADHD-like behavior were recorded, as well as of a control group of 12 dogs with no reported behavioral problems. Computational analysis with a self-developed tool based on computer vision and machine learning was performed, analyzing 12 movement parameters that can be extracted from automatic dog tracking data. Significant differences in seven movement parameters were found, which led to the identification of three dimensions of movement patterns which may be instrumental for more objective assessment of ADHD-like behavior by clinicians, while being directly observable during consultation. These include (i) high speed, (ii) large coverage of space, and (iii) constant re-orientation in space. Computational tools used on video data collected during consultation have the potential to support quantifiable assessment of ADHD-like behavior informed by the identified dimensions