Visualisation of Sensor Data from Animal Movement

Abstract:

A new area of biological research is identifying and grouping patterns of behaviour in wild animals by analysing data obtained through the attachment of tri-axial accelerometers. As these recording devices become smaller and less expensive their use has increased. Currently acceleration data are visualised as 2D time series plots, and analyses are based on summary statistics and the application of Fourier transforms. We develop alternate visualisations of this data so as to analyse, explore and present new patterns of animal behaviour. Our visualisations include interactive spherical scatterplots, spherical histograms, clustering methods, and feature-based state diagrams of the data. We study the application of these visualisation methods to accelerometry data from animal movement. The reaction of biologists to these visualisations is also reported.

Paper(s):

Edward Grundy, Mark W. Jones, Robert S. Laramee, Rory P. Wilson, and Emily L. C. Shepard, Visualisation of Sensor Data from Animal Movement, Computer Graphics Forum (CGF), Vol. 28, No. 3, 2009, pages 815-822 (Proceedings of EuroVis 2009, June 10 - 12, 2009, Berlin, Germany) ( PDF file, MPEG Video ) -Recipient of the Best Paper Award at EuroVis 2009.

Links:

New Scientist Article featuring Grundy's nice work.
Infosthetics Article on the EuroVis 2009 Conference.
Charl Botha's EuroVis 2009 conference report.
Enrico Bertini's EuroVis 2009 conference report.

Supplementary Images and MPEGs
(Click on images for MPEG animations)

Interactive Brushing and Clustering

A state transition diagram of different animal behaviors.

Visualization of combined acceleration and pressure.

Interactive polar histograms of acceleration.

Interactive brushing of state probabilities.

This visualization shows an animation of animal orientation.

Body motion: This data contains an extra attribute indicating the path the animal has travelled, allowing a full reconstruction of movement behaviour. Because the sensor was attached near the tail of the fish, tail beats are present in the data. By taking a moving average of the location, we can deform the mesh based on the difference between the moving average (which we assume to be the body) and the raw data (which we assume is the tail), giving a simple representation of body movements.

An animation of the whaleshark swimming.