4.5 Article

Dog behaviour classification with movement sensors placed on the harness and the collar

期刊

APPLIED ANIMAL BEHAVIOUR SCIENCE
卷 241, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.applanim.2021.105393

关键词

Dog; Canine; Behaviour classification; Actigraphy; Accelerometry; Activity monitoring; Wearable technology

资金

  1. Business Finland, a Finnish funding agengy for innovation [1665/31/2016, 1894/31/2016, 7244/31/2016]

向作者/读者索取更多资源

Using accelerometer and gyroscope data, supervised machine learning methods can accurately classify seven typical dog activities. The movement sensor on the back performed better in classification accuracy compared to the sensor on the collar.
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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据