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The Development of a Horse Activity Recognition Algorithm

Huveneers, Maartje (2021) The Development of a Horse Activity Recognition Algorithm.

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Abstract:Animal activity recognition is a growing field of research, which can help with the monitoring of wildlife and their habitats. For portability and accuracy, the monitoring can be done using devices that contain sensors, such as an Inertial Measurement Unit (IMU). To recognize the activities that the animals are performing from the extracted data from the IMU, a well-performing algorithm is needed. Thus, the following research question has been researched in this report: Which animal activity recognition algorithm performs the best on IMU horse data, and what aspects lead it to outperform other algorithms? To answer this question, a pipeline was developed with the IMU horse data as input and analyzed using three deep learning algorithms: a deep Neural Network (NN), a Long Short-Term Memory (LSTM) and a Multivariate Long Short-Term Memory Fully Convolutional Network (MLSTM-FCN), based upon the state of the art. The three classifiers were compared with each other, in which the LSTM (87.8% accuracy, 88.4% F-score) and MLSTM-FCN (88.5% accuracy, 87.6% F-score) yielded a higher performance than the NN (82.8% accuracy, 82.3% F-score) on all metrics (accuracy, balanced accuracy, F-score and MCC) and are thus recommended to use in future animal activity recognition projects.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Creative Technology BSc (50447)
Link to this item:http://purl.utwente.nl/essays/86921
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