Dog activity classification with movement sensor placed on the collar

Pekka Kumpulainen, Anna Valldeoriola, Sanni Somppi, Heini Törnqvist, Heli Väätäjä, Päivi Majaranta, Veikko Surakka, Outi Vainio, Miiamaaria V. Kujala, Yulia Gizatdinova, Antti Vehkaoja

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Dog owners are highly motivated in understanding behavior and physiology of their pets and monitoring their wellbeing. Monitoring with a commercially available activity trackers reveals levels of daily activity and rest but recognizing the behavior of the dog would provide additional information, especially when the dog is not under supervision. In this study, a performance of a 3D accelerometer movement sensor placed on the dog collar was evaluated in classifying seven activities during semi-controlled test situation with 24 dogs. Various features were extracted from the acceleration time series signals. The performance of two classifiers was evaluated with two feature scenarios: using all computed features and the ones given by forward selection algorithm. The highest overall classification accuracy for the seven behaviors was 76%. The results are promising pro improving classification of specific behaviors by relatively simple algorithms.

Original languageEnglish
Title of host publicationACI 2018
Subtitle of host publicationProceedings of the Fifth International Conference on Animal-Computer Interaction
ISBN (Electronic)9781450362191
Publication statusPublished - 4 Dec 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Animal-Computer Interaction - Georgia Tech., Atlanta, United States
Duration: 4 Dec 20186 Dec 2018
Conference number: 5


ConferenceInternational Conference on Animal-Computer Interaction
Abbreviated titleACI
CountryUnited States
Internet address


  • Accelerometer
  • Activity monitoring
  • Canine

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