TitleCreating a behavioural classification module for acceleration data: using a captive surrogate for difficult to observe species
Publication TypeJournal Article
Year of Publication2013
AuthorsCampbell HA, Gao L, Bidder OR, Hunter J, Franklin CE
Volume216
Pagination4501-4506
Date PublishedDec
Type of ArticleArticle
ISBN Number0022-0949
Accession NumberBIOSIS:PREV201400062603
Keywords07002, Behavioral biology - General and comparative behavior, 07003,, Animals,, Behavior, Behavioral biology - Animal behavior, behavioural mode, behavioural classification module, acceleration data, Canidae [85765], Carnivora, Mammalia, Vertebrata, Chordata, Animalia, Carnivores, Chordates, Mammals, Nonhuman Vertebrates, Nonhuman Mammals,, computer techniques, laboratory equipment/binary classification system, laboratory techniques/tri-axial accelerometer, mathematical and, mathematical and computer, Methods and Techniques, module, support vector machine, techniques/biotelemetry, Vertebrates, [dog]
AbstractDistinguishing specific behavioural modes from data collected by animal-borne tri-axial accelerometers can be a time-consuming and subjective process. Data synthesis can be further inhibited when the tri-axial acceleration data cannot be paired with the corresponding behavioural mode through direct observation. Here, we explored the use of a tame surrogate (domestic dog) to build a behavioural classification module, and then used that module to accurately identify and quantify behavioural modes within acceleration collected from other individuals/species. Tri-axial acceleration data were recorded from a domestic dog whilst it was commanded to walk, run, sit, stand and lie-down. Through video synchronisation, each tri-axial acceleration sample was annotated with its associated behavioural mode; the feature vectors were extracted and used to build the classification module through the application of support vector machines (SVMs). This behavioural classification module was then used to identify and quantify the same behavioural modes in acceleration collected from a range of other species (alligator, badger, cheetah, dingo, echidna, kangaroo and wombat). Evaluation of the module performance, using a binary classification system, showed there was a high capacity (>90%) for behaviour recognition between individuals of the same species. Furthermore, a positive correlation existed between SVM capacity and the similarity of the individual's spinal length-to-height above the ground ratio (SL:SH) to that of the surrogate. The study describes how to build a behavioural classification module and highlights the value of using a surrogate for studying cryptic, rare or endangered species.