Give the machine a hand: A Boolean time‐based decision‐tree template for rapidly finding animal behaviours in multisensor data
R.P. Wilson, M.D. Holton, A. di Virgilio, H. Williams, E.L.C. Shepard, S. Lambertucci, F. Quintana, J.E. Sala, B. Balaji, E. Sun Lee, M. Srivastava, C.M. Duarte
Methods Ecol Evol., (2018)
Accelerometer, Behaviour, Behaviour identification, Bioinformatics, Software
- The development of multisensor animal‐attached tags,
recording data at high frequencies, has enormous potential in allowing
us to define animal behaviour.
- The high volumes of data, are pushing us towards
machine‐learning as a powerful option for distilling out behaviours.
However, with increasing parallel lines of data, systems become more
likely to become processor limited and thereby take appreciable amounts
of time to resolve behaviours.
- We suggest a Boolean approach whereby critical
changes in recorded parameters are used as sequential templates with
defined flexibility (in both time and degree) to determine individual
behavioural elements within a behavioural sequence that, together, makes
up a single, defined behaviour.
- We tested this approach, and compared it to a suite
of other behavioural identification methods, on a number of behaviours
from tag‐equipped animals; sheep grazing, penguins walking, cheetah
stalking prey and condors thermalling.
- Overall behaviour recognition using our new approach
was better than most other methods due to; (1) its ability to deal with
behavioural variation and (2) the speed with which the task was
completed because extraneous data are avoided in the process.
- We suggest that this approach is a promising way
forward in an increasingly data‐rich environment and that workers
sharing algorithms can provide a powerful library for the benefit of all
involved in such work.
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