engleski

LABAQM - A System for Qualitative Modelling and Analysis of Animal Behaviour

Tracking of a laboratory animal and its behaviour interpretation based on frame sequence analysis have been traditionally quantitative and typically generates large amounts of temporally evolving data. In our work we are dealing with higher-level approaches such as conceptual clustering and qualitative modelling in order to represent data obtained by tracking. We present the LABAQM system developed for the analysis of laboratory animal behaviours. It is based on qualitative modelling of animal motions. We are dealing with the cognitive phase of the laboratory animal behaviour analysis as a part of the pharmacological experiments. The system is based on the quantitative data from the tracking application and incomplete domain background knowledge. The LABAQM system operates in two main phases: behaviour learning and behaviour analysis. The behaviour learning and behaviour analysis phase are based on symbol sequences, obtained by the transformation of the quantitative data. Behaviour learning phase includes supervised learning procedure, unsupervised learning procedure and their combination. The fusion of supervised and unsupervised learning procedures produces more robust models of characteristic behaviours, which are used in the behaviour analysis phase.

dynamic vision system; qualitative modelling; conceptual clustering; hidden Markov models of characteristic behaviours

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