Abstract
The miniaturizations of sensing units, the increase in storage capacity, and the longevity of batteries, as well as the advancement of big-data processing technologies, are making it possible to recognize animal behaviors. This allows researchers to understand animal space use patterns, social interactions, habitats, etc. In this study, we focused on the behavior recognition of Asian black bears (Ursus thibetanus) using a three-axis accelerometer embedded in collars attached to their necks, where approximately 1% of data obtained from four bears over an average of 42 d were used. A machine learning was used to recognize seven bear behaviors, where oversampling and extension of labels to the period adjacent to the labeled period were applied to overcome data imbalance across classes and insufficient data in some classes. Experimental results showed the effectiveness of oversampling and a large difference in individual bears. Effective feature sets vary by experimental conditions. However, a tendency of features calculated from the magnitude of the three axes contributing to classification performance was confirmed.
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Notes
- 1.
In human behavior recognition, it is called leave-one-subject-out CV or leave-one-person-out CV.
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This work was supported fully by TAMAGO program of Tokyo University of Agriculture and Technology and partly by JSPS KAKENHI Grant (Number 17H05971).
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Fujinami, K., Naganuma, T., Shinoda, Y., Yamazaki, K., Koike, S. (2022). Attempts Toward Behavior Recognition of the Asian Black Bears Using an Accelerometer. In: Ahad, M.A.R., Inoue, S., Roggen, D., Fujinami, K. (eds) Sensor- and Video-Based Activity and Behavior Computing. Smart Innovation, Systems and Technologies, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-19-0361-8_4
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