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

*Kontaktforfatter for dette arbejde

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

7 Downloads (Pure)


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.

TidsskriftAnimal Biotelemetry
Udgave nummer1
Antal sider21
StatusUdgivet - dec. 2021

Bibliografisk note

Funding Information:
This work funded with NSF Grants IOS-1656691 and OPP-1643877, ONR YIP Grant #N000141612477, Grants from the World Wildlife Fund, and Stanford University’s Terman and Bass Fellowships. Funds raised from workshop registration were used to support a paid high-school internship program at Stanford University and included a grant from the National Marine Sanctuary Foundation and individual donations from workshop participants.

Funding Information:
Thanks to Mark Johnson and Stacy DeRuiter and others who have worked on DTAG tools for setting the stage for this work over the years, including designing the Animal Tags Project at , and running a workshop in October 2017 on utilizing tag tools attended by several authors. Thanks also to Nikolai Liebsch and Peter Kraft at CATS for their continuous work to improve the devices that make hypothesis-driven bio-logging possible. Thanks to Jim Harvey, Alison Stimpert and Moss Landing Marine Labs for supporting and participating in field efforts in Monterey Bay, and to Kakani Katija and MBARI for use of their pressure chamber. Thanks to Jessica Bender for the illustrations used in Figs. 4 and 7. Thanks also to the workshop participants in Dec 2020 who gave a week of their time to learn these tools and along the way provided valuable feedback on our code and instructional practices. This group includes Taylor Azizeh, Ellen Chenoweth, Leah Crowe, Jacopo Di Clemente, Julia Dombroski, Arina Favilla, Elise Keppel, Jessica Kendall-Bar, Theresa Kirchner, Jessica Kittel, Marc Lammers, Sarah Luongo, Morgan Martin, Raphael Mayaud, Alexandra McInturf, Christie McMillan, Cameron Perry, Nicola Quick, Rhonda Reidy, Kerri Seger, Anna Selbmann, Jeanne Shearer, Andy Szabo, Jenn Tackaberry, Emma Vogel, Mason Weinrich, Suzie Winquist, Eden Zang, and Julia Zeh.

Publisher Copyright:
© 2021, The Author(s).


Dyk ned i forskningsemnerne om 'Tools for integrating inertial sensor data with video bio-loggers, including estimation of animal orientation, motion, and position'. Sammen danner de et unikt fingeraftryk.