Abstract
Dogs are one of the most popular pets in the world, and more than 10 million dogs are bred annually in Japan now [4]. Recently, primitive commercial services have been started that record dogs’ activities and report them to their owners. Although it is expected that an owner would like to know the dog’s activity in greater detail, a method proposed in a previous study has failed to recognize some of the key actions. The demand for their identification is highlighted in responses to our questionnaire. In this paper, we show a method to recognize the actions of the dog by attaching only one off-the-shelf acceleration sensor to the neck of the dog. We apply DTW-D which is the state-of-the-art time series data search technique for activity recognition. Application of DTW-D to activity recognition of an animal is unprecedented according to our knowledge, and thus is the main contribution of this study. As a result, we were able to recognize eleven different activities with 75.1 % classification F-measure. We also evaluate the method taking account of real-world use cases.
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Acknowledgements
This work was supported by JSPS KAKENHI Grant Numbers 24300005, 26330081, 26870201.
In performing this study, We would like to thank everyone that has helped us questionnaire survey.
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Kiyohara, T., Orihara, R., Sei, Y., Tahara, Y., Ohsuga, A. (2015). Activity Recognition for Dogs Based on Time-series Data Analysis. In: Duval, B., van den Herik, J., Loiseau, S., Filipe, J. (eds) Agents and Artificial Intelligence. ICAART 2015. Lecture Notes in Computer Science(), vol 9494. Springer, Cham. https://doi.org/10.1007/978-3-319-27947-3_9
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DOI: https://doi.org/10.1007/978-3-319-27947-3_9
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