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

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.

Evidence for Size-Selective Predation by Antarctic Humpback Whales

“…Tag accelerometers for all deployments were sampled at 400 Hz, magnetometers and gyroscopes at 50 Hz, and pressure, light, temperature and GPS at 10 Hz. All data were decimated to 10 Hz, tag orientation on the animal was corrected for, and animal orientation was calculated using custom-written scripts in Matlab 2014a (following Cade et al, 2021a). Animal speed for all deployments was determined using the amplitude of tag vibrations (Cade et al, 2018), and animal positions for the duration of each deployment were estimated from interpolating pseudotracks of the animal between known fast-acquisition GPS positions collected by the tags when the whales were at the surface and distributing accumulated error along the track (Wilson et al, 2007).…”

Characterizing the suckling behavior by video and 3D-accelerometry in humpback whale calves on a breeding ground

“…Data from all sensors were downloaded as CSV files and imported into MATLAB (Mathworks) using dedicated scripts ( CATS Matlab toolkit , https://github.com/wgough/CATS-Methods-Materials , Cade et al, 2021 ). Raw accelerometer data were downsampled to obtain a common sampling rate of 10 Hz across all sensors and depth data (in meters) was smoothed with a 0.5 s running median filter.…”

Pose-gait analysis for cetaceans with biologging tags

“…Because many cetacean tags use a suction cup-based attachment method, the location and orientation of the tag wrt the animal can change during field deployments [1, 25]. As a result, the relative orientation of the tag wrt the animal must be determined before animal pose calculation [1, 8, 26]. Further, even with the best practices for deploying tags [25], the relative orientation between tag and animal can change during a deployment (i.e.…”

“…For example, when an animal breathes at the surface (surfacing), it is assumed that the pose should not have significant roll. A surfacing event can be inferred from pressure data and the roll estimate can then be corrected accordingly [1, 8, 26]. Tag orientation shifts tend to be identified by human inspection using sensor streams [1, 26], like accelerometer data, to identify features, such as an impact to the tag by another animal or object.…”

“…A surfacing event can be inferred from pressure data and the roll estimate can then be corrected accordingly [1, 8, 26]. Tag orientation shifts tend to be identified by human inspection using sensor streams [1, 26], like accelerometer data, to identify features, such as an impact to the tag by another animal or object. However, the tag may be impacted by hydrodynamic forces, which can result in slowly changing orientation (slides) that are not captured by an accelerometer.…”