Animal Biotelemetry (Aug 2021)

Tools for integrating inertial sensor data with video bio-loggers, including estimation of animal orientation, motion, and position

  • David E. Cade,
  • William T. Gough,
  • Max F. Czapanskiy,
  • James A. Fahlbusch,
  • Shirel R. Kahane-Rapport,
  • Jacob M. J. Linsky,
  • Ross C. Nichols,
  • William K. Oestreich,
  • Danuta M. Wisniewska,
  • Ari S. Friedlaender,
  • Jeremy A. Goldbogen

DOI
https://doi.org/10.1186/s40317-021-00256-w
Journal volume & issue
Vol. 9, no. 1
pp. 1 – 21

Abstract

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Abstract Bio-logging devices equipped with inertial measurement units—particularly accelerometers, magnetometers, and pressure sensors—have revolutionized our ability to study animals as necessary electronics have gotten smaller and more affordable over the last two decades. These animal-attached tags allow for fine scale determination of behavior in the absence of direct observation, particularly useful in the marine realm, where direct observation is often impossible, and recent devices can integrate more power hungry and sensitive instruments, such as hydrophones, cameras, and physiological sensors. To convert the raw voltages recorded by bio-logging sensors into biologically meaningful metrics of orientation (e.g., pitch, roll and heading), motion (e.g., speed, specific acceleration) and position (e.g., depth and spatial coordinates), we developed a series of MATLAB tools and online instructional tutorials. Our tools are adaptable for a variety of devices, though we focus specifically on the integration of video, audio, 3-axis accelerometers, 3-axis magnetometers, 3-axis gyroscopes, pressure, temperature, light and GPS data that are the standard outputs from Customized Animal Tracking Solutions (CATS) video tags. Our tools were developed and tested on cetacean data but are designed to be modular and adaptable for a variety of marine and terrestrial species. In this text, we describe how to use these tools, the theories and ideas behind their development, and ideas and additional tools for applying the outputs of the process to biological research. We additionally explore and address common errors that can occur during processing and discuss future applications. All code is provided open source and is designed to be useful to both novice and experienced programmers.

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