A spherical-plot solution to linking acceleration metrics with animal performance, state, behaviour and lifestyle

Abstract

BackgroundWe are increasingly using recording devices with multiple sensors operating at high frequencies to produce large volumes of data which are problematic to interpret. A particularly challenging example comes from studies on animals and humans where researchers use animal-attached accelerometers on moving subjects to attempt to quantify behaviour, energy expenditure and condition.ResultsThe approach taken effectively concatinated three complex lines of acceleration into one visualization that highlighted patterns that were otherwise not obvious. The summation of data points within sphere facets and presentation into histograms on the sphere surface effectively dealt with data occlusion. Further frequency binning of data within facets and representation of these bins as discs on spines radiating from the sphere allowed patterns in dynamic body accelerations (DBA) associated with different postures to become obvious.MethodWe examine the extent to which novel, gravity-based spherical plots can produce revealing visualizations to incorporate the complexity of such multidimensional acceleration data using a suite of different acceleration-derived metrics with a view to highlighting patterns that are not obvious using current approaches. The basis for the visualisation involved three-dimensional plots of the smoothed acceleration values, which then occupied points on the surface of a sphere. This sphere was divided into facets and point density within each facet expressed as a histogram. Within each facet-dependent histogram, data were also grouped into frequency bins of any desirable parameters, most particularly dynamic body acceleration (DBA), which were then presented as discs on a central spine radiating from the facet. Greater radial distances from the sphere surface indicated greater DBA values while greater disc diameter indicated larger numbers of data points with that particular value.ConclusionsWe indicate how this approach links behaviour and proxies for energetics and can inform our identification and understanding of movement-related processes, highlighting subtle differences in movement and its associated energetics. This approach has ramifications that should expand to areas as disparate as disease identification, lifestyle, sports practice and wild animal ecology.UCT Science Faculty Animal Ethics 2014/V10/PR (valid until 2017).Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-016-0088-3) contains supplementary material, which is available to authorized users.

Identification of animal movement patterns using tri-axial magnetometry

“…Indeed, given the tri-axial and orthogonal properties of accelerometer and magnetometer data, it makes sense for both data types to be visualised in tri-axial space. Wilson et al [35] demonstrate the use of the g-sphere to help interpret accelerometry data illustrating variation in posture and we adapt this approach. It is important to note, however, that the interpretation of g-spheres and m-spheres differs fundamentally, due to their different frames of reference and the insensitivity of TriMag sensors to dynamic acceleration.…”

“…in preparation). The g-spheres ( left ) are 3-dimensional plots of static acceleration data, where the distribution of points illustrates the range in postural orientation of the animal in 3D space (see [35]). By comparison, the m-spheres ( middle ) also show postural information in angular rotation, but reveal how posture varies according to heading.…”

“…Second, we provide a method of calibrating the TriMag sensor to the Earth’s magnetic field according to the normal orientation of the carrier animal. Third, we go on to propose the use of tri-axial plots to help interpret TriMag data, following the utility of this approach for other orthogonally mounted sensor data [35]. This approach visualises the data and allows angular rotations to be easily resolved.…”

A comparison of techniques for classifying behavior from accelerometers for two species of seabird

“…A host of methods have been proposed for classifying animal behavior from accelerometer data (Appendix S1), including movement thresholds (Brown et al, 2013;Moreau, Siebert, Buerkert, & Schlecht, 2009;Shamoun-Baranes et al, 2012), histogram analysis (Collins et al, 2015), k-means (KM) cluster analysis (Angel, Berlincourt, & Arnould, 2016;Sakamoto et al, 2009), k-nearest neighbor analysis (Bidder et al, 2014), classification and regression trees (Shamoun-Baranes et al, 2012), neural networks (NN; Nathan et al, 2012;Resheff, Rotics, Harel, Spiegel, & Nathan, 2014), random forests (Bom, Bouten, Piersma, Oosterbeek, & van Gils, 2014;Nathan et al, 2012;Pagano et al, 2017), hidden Markov models (HMM; Leos-Barajas et al, 2016), expectation maximization (EM; Chimienti et al, 2016), and super machine learning (Ladds et al, 2017). At least three custom software applications are available for classifying animal behavior from trained accelerometer data: AcceleRater (Resheff et al, 2014), G-sphere (Wilson et al, 2016), and Ethographer (Sakamoto et al, 2009). Many of these methods use machine-learning techniques that are difficult to interpret because underlying processes are opaque.…”

Activity budgets for the sedentary Argentine sea bass Acanthistius patachonicus inferred from accelerometer data loggers

“…Additionally, urchin graphs (Wilson et al . ) were plotted to visually compare the four different behaviours between warm and cold seasons. The process for creating this visualisation is as follows: first, the smoothed acceleration values are plotted in a tri‐axial graph, which results in each point falling on the surface of a sphere.…”