CompanionViz: Mediated platform for gauging canine health and enhancing human–pet interactions

Jonathan K. Nelson, Patrick C. Shih

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Advancements in personal data collection and visualization – commonly referred to as the quantified self (QS) movement – allow individuals to self-track health and other attributes. We extend quantified self (QS) concepts to the quantified other (QO) to explore how the use of technology, collection of data on one's pet dog, and personal visualization affect pet owners' understandings of, and relationships with, their pets. We introduce the term Human–pet–computer interaction (HPCI) as the study of how technology can be designed and used to advance human–pet companionships. As an example, we describe CompanionViz, a personal information visualization prototype designed to inform pet owners on their dogs’ caloric inputs/outputs, as well as exercise and movement habits. We present a user study of CompanionViz featuring a twelve-participant survey and one field study, consisting of three unique use cases, and show that by providing pet owners with quantifiable awareness of their dogs’ health and exercise habits using personal visual representations, pet owner–dog bonds can benefit.

Original languageEnglish (US)
Pages (from-to)169-178
Number of pages10
JournalInternational Journal of Human Computer Studies
Volume98
DOIs
StatePublished - Feb 1 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Human Factors and Ergonomics
  • Education
  • Engineering(all)
  • Human-Computer Interaction
  • Hardware and Architecture

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