Sensor Fusion for Unobtrusive Respiratory Rate Estimation in Dogs

C.H. Antink, Mikko Pirhonen, Heli Väätäjä, Sanni Somppi, Heini Tornqvist, Anna Cardo, Daniel Teichmann, Outi Vainio, Veikko Surakka, Antti Vehkaoja

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
44 Downloads (Pure)


Respiration is vital to land-dwelling mammals: besides, salient information is encoded in the respiratory rate. Objective assessment of the respiratory rate is difficult in dogs: in particular, if the unobtrusive measurement is desired. The goal of this work was to develop and evaluate a method for unobtrusive sensing of respiratory rate in dogs. For this, the 'FlexPock' multisensor system, originally developed for unobtrusive estimation of heart rate and respiratory rate in humans via magnetic impedance: accelerometry: and optical measurements, was used to assess canine respiratory rate. In a proof-of-concept study with 10 healthy dogs of different breeds and sizes, a total of 240 minutes of data was recorded in the phases standing, sitting, lying down, and walking. An algorithm was developed that estimates the respiratory rate by fusing the information from multiple sensors for increased accuracy and robustness. To discard unusable data, a simple yet effective signal quality metric was introduced. Impedance pneumography recorded using adhesive electrodes was used as a reference. Analysis of the raw FlexPock data revealed that the magnetic impedance and accelerometry were the best individual sensing modalities and fusion of these data further increased the accuracy. Using leave-one-dog-out cross-validation, the average estimation error was 9.5% at a coverage of 50.1%. However, strong variation between dogs and phases was observed. During the walking phase, neither reference nor unobtrusive sensor reported usable results, while the sitting phase exhibited the best performance. In conclusion, the fusion of magnetic impedance and accelerometry can be used for unobtrusive respiratory rate estimation in stationary dogs.

Original languageEnglish
Article number8693797
Pages (from-to)7072-7081
Number of pages10
JournalIEEE Sensors Journal
Issue number16
Publication statusPublished - 2019
Publication typeA1 Journal article-refereed


  • Sensor fusion
  • animal health management
  • dogs
  • monitoring
  • respiratory rate
  • unobtrusive sensing

Publication forum classification

  • Publication forum level 2


Dive into the research topics of 'Sensor Fusion for Unobtrusive Respiratory Rate Estimation in Dogs'. Together they form a unique fingerprint.

Cite this