1D-CNN-LSTM Hybrid-Model-Based Pet Behavior Recognition through Wearable Sensor Data Augmentation


Hyungju Kim, Nammee Moon, Journal of Information Processing Systems Vol. 20, No. 2, pp. 159-172, Apr. 2024  

https://doi.org/10.3745/JIPS.02.0211
Keywords: Behavior Recognition, CNN-LSTM, data augmentation, Deep Learning, Sensor data, wearable device
Fulltext:

Abstract

The number of healthcare products available for pets has increased in recent times, which has prompted active research into wearable devices for pets. However, the data collected through such devices are limited by outliers and missing values owing to the anomalous and irregular characteristics of pets. Hence, we propose pet behavior recognition based on a hybrid one-dimensional convolutional neural network (CNN) and long shortterm memory (LSTM) model using pet wearable devices. An Arduino-based pet wearable device was first fabricated to collect data for behavior recognition, where gyroscope and accelerometer values were collected using the device. Then, data augmentation was performed after replacing any missing values and outliers via preprocessing. At this time, the behaviors were classified into five types. To prevent bias from specific actions in the data augmentation, the number of datasets was compared and balanced, and CNN-LSTM-based deep learning was performed. The five subdivided behaviors and overall performance were then evaluated, and the overall accuracy of behavior recognition was found to be about 88.76%.


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Cite this article
[APA Style]
Kim, H. & Moon, N. (2024). 1D-CNN-LSTM Hybrid-Model-Based Pet Behavior Recognition through Wearable Sensor Data Augmentation. Journal of Information Processing Systems, 20(2), 159-172. DOI: 10.3745/JIPS.02.0211.

[IEEE Style]
H. Kim and N. Moon, "1D-CNN-LSTM Hybrid-Model-Based Pet Behavior Recognition through Wearable Sensor Data Augmentation," Journal of Information Processing Systems, vol. 20, no. 2, pp. 159-172, 2024. DOI: 10.3745/JIPS.02.0211.

[ACM Style]
Hyungju Kim and Nammee Moon. 2024. 1D-CNN-LSTM Hybrid-Model-Based Pet Behavior Recognition through Wearable Sensor Data Augmentation. Journal of Information Processing Systems, 20, 2, (2024), 159-172. DOI: 10.3745/JIPS.02.0211.