Using Tri-Axial Acceleration Data To Predict Behavioral Modes Of The Domestic Cat (Felis catus)

Advisor Information

Bruce Chase

Location

MBSC Ballroom - Poster #609 - G

Presentation Type

Poster

Start Date

4-3-2022 2:00 PM

End Date

4-3-2022 3:15 PM

Abstract

The use of accelerometers has proved to be a powerful tool for predicting animal behavior. However, while bio-logging techniques are being used on many species, we do not have enough data to predict the cat behavior utilizing this technique. The lack of such data is likely due to the difficulty of observing domestic cats outside for an extended period of time. Here, we attempt to overcome this obstacle and propose a deep learning methodology for identifying and predicting behaviors from accelerometer data of indoor-outdoor domestic cats.

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Mar 4th, 2:00 PM Mar 4th, 3:15 PM

Using Tri-Axial Acceleration Data To Predict Behavioral Modes Of The Domestic Cat (Felis catus)

MBSC Ballroom - Poster #609 - G

The use of accelerometers has proved to be a powerful tool for predicting animal behavior. However, while bio-logging techniques are being used on many species, we do not have enough data to predict the cat behavior utilizing this technique. The lack of such data is likely due to the difficulty of observing domestic cats outside for an extended period of time. Here, we attempt to overcome this obstacle and propose a deep learning methodology for identifying and predicting behaviors from accelerometer data of indoor-outdoor domestic cats.