TY - JOUR
T1 - Dog behaviour classification with movement sensors placed on the harness and the collar
AU - Kumpulainen, Pekka
AU - Cardó, Anna Valldeoriola
AU - Somppi, Sanni
AU - Törnqvist, Heini
AU - Väätäjä, Heli
AU - Majaranta, Päivi
AU - Gizatdinova, Yulia
AU - Hoog Antink, Christoph
AU - Surakka, Veikko
AU - Kujala, Miiamaaria V.
AU - Vainio, Outi
AU - Vehkaoja, Antti
N1 - Funding Information:
The authors would like to thank all the participants of the study. This research was funded by Business Finland, a Finnish funding agengy for innovation, grant numbers 1665/31/2016 , 1894/31/2016 , 7244/31/2016 in the context of “Buddy and the Smiths 2.0” project.
Publisher Copyright:
© 2021 The Authors
PY - 2021/7/1
Y1 - 2021/7/1
N2 - Dog owners’ understanding of the daily behaviour of their dogs may be enhanced by movement measurements that can detect repeatable dog behaviour, such as levels of daily activity and rest as well as their changes. The aim of this study was to evaluate the performance of supervised machine learning methods utilising accelerometer and gyroscope data provided by wearable movement sensors in classification of seven typical dog activities in a semi-controlled test situation. Forty-five middle to large sized dogs participated in the study. Two sensor devices were attached to each dog, one on the back of the dog in a harness and one on the neck collar. Altogether 54 features were extracted from the acceleration and gyroscope signals divided in two-second segments. The performance of four classifiers were compared using features derived from both sensor modalities. and from the acceleration data only. The results were promising; the movement sensor at the back yielded up to 91 % accuracy in classifying the dog activities and the sensor placed at the collar yielded 75 % accuracy at best. Including the gyroscope features improved the classification accuracy by 0.7–2.6 %, depending on the classifier and the sensor location. The most distinct activity was sniffing, whereas the static postures (lying on chest, sitting and standing) were the most challenging behaviours to classify, especially from the data of the neck collar sensor. The data used in this article as well as the signal processing scripts are openly available in Mendeley Data, https://doi.org/10.17632/vxhx934tbn.1.
AB - Dog owners’ understanding of the daily behaviour of their dogs may be enhanced by movement measurements that can detect repeatable dog behaviour, such as levels of daily activity and rest as well as their changes. The aim of this study was to evaluate the performance of supervised machine learning methods utilising accelerometer and gyroscope data provided by wearable movement sensors in classification of seven typical dog activities in a semi-controlled test situation. Forty-five middle to large sized dogs participated in the study. Two sensor devices were attached to each dog, one on the back of the dog in a harness and one on the neck collar. Altogether 54 features were extracted from the acceleration and gyroscope signals divided in two-second segments. The performance of four classifiers were compared using features derived from both sensor modalities. and from the acceleration data only. The results were promising; the movement sensor at the back yielded up to 91 % accuracy in classifying the dog activities and the sensor placed at the collar yielded 75 % accuracy at best. Including the gyroscope features improved the classification accuracy by 0.7–2.6 %, depending on the classifier and the sensor location. The most distinct activity was sniffing, whereas the static postures (lying on chest, sitting and standing) were the most challenging behaviours to classify, especially from the data of the neck collar sensor. The data used in this article as well as the signal processing scripts are openly available in Mendeley Data, https://doi.org/10.17632/vxhx934tbn.1.
KW - Accelerometry
KW - Actigraphy
KW - Activity monitoring
KW - Behaviour classification
KW - Canine
KW - Dog
KW - Wearable technology
U2 - 10.1016/j.applanim.2021.105393
DO - 10.1016/j.applanim.2021.105393
M3 - Article
AN - SCOPUS:85109439997
VL - 241
JO - APPLIED ANIMAL BEHAVIOUR SCIENCE
JF - APPLIED ANIMAL BEHAVIOUR SCIENCE
SN - 0168-1591
M1 - 105393
ER -