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Abstract
Studies of locomotion kinematics require high-resolution information about body movements and the specific acceleration (SA) that these generate. On-animal accelerometers measure both orientation and SA but an additional orientation sensor is needed to accurately separate these. Although gyroscopes can perform this function, their power consumption, drift and complex data processing make them unattractive for biologging. Lower power magnetometers can also be used with some limitations. Here, we present an integrated and simplified method for estimating body rotations and SA applicable to both gyroscopes and magnetometers, enabling a direct comparison of these two sensors. We use a tag with both sensors to demonstrate how caudal-oscillation rate and SA are adjusted by a diving whale in response to rapidly changing buoyancy forces as the lungs compress while descending. Both sensors gave similar estimates of the dynamic forces demonstrating that magnetometers may offer a simpler low-power alternative for miniature tags in some applications.
Original language | English |
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Pages (from-to) | 2103-2109 |
Journal | Journal of Experimental Biology |
Volume | 219 |
Issue number | 14 |
Early online date | 20 Jul 2016 |
DOIs | |
Publication status | Published - Jul 2016 |
Keywords
- Gyroscope
- Accelerometer
- Magnetometer
- Specific acceleration
- Body rotation
- Swimming kinematics
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Dive into the research topics of 'Tracking the kinematics of caudal-oscillatory swimming: a comparison of two on-animal sensing methods'. Together they form a unique fingerprint.Projects
- 1 Finished
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Marie Curie CIG Animal Sound Sensors: Marie Curie CIG Animal Sound Sensors
Johnson, M. (PI) & Boyd, I. (CoI)
1/03/12 → 28/02/16
Project: Standard
Datasets
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Software and example data underpinning 'Tracking the kinematics of caudal-oscillatory swimming: a comparison of two on-animal sensing methods'
Johnson, M. (Creator), Martin Lopez, L. M. (Contributor) & Aguilar de Soto, N. (Data Collector), University of St Andrews, 2016
DOI: 10.17630/9e38ff35-341d-442f-9f04-c63b7a48bc8b
Dataset
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