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Turning Detection in Sandbar Sharks Through Accelerometer Data

Powell, Benjamin
Abstract
This paper introduces a novel method for classifying turning in sandbar sharks from accelerometer data. Because marine organisms are difficult to observe visually, attachable tags are often used instead, typically including only an accelerometer for power reasons; measuring complex motions, such as turning, is difficult without a gyroscope. Six features, including a novel metric designed to detect turning direction, are used. To deal with sample imbalance, a mixture of SMOTE and modified bootstrap sampling is employed. The classifier has 79\% accuracy and can highlight periods of heightened turning activity as well as determine the direction of swimming.
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2018-05-01
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paper_draft_1.pdf
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Zhou, Gang
Weng, Kevin
Poshyvanyk, Denys
Sanderson, S. Laurie
Weng, Timothy Davis, Kevin
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Computer Science
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https://scholarworks.wm.edu/handle/internal/10546
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