Bielefeld University, Germany
User behavior recognition, Task analysis, Bayesian network, Bayesian filtering.
Computer Vision, Visualization and Computer Graphics
Image and Video Analysis
Learning of Action Patterns
In this paper, we describe a structured approach for user behavior recognition in an automatic prompting system
that assists users with cognitive disabilities in the task of brushing their teeth. We analyze the brushing task
using qualitative data analysis. The results are a hierarchical decomposition of the task and the identification
of environmental configurations during subtasks. We develop a hierarchical recognition framework based on
the results of task analysis: We extract a set of features from multimodal sensors which are discretized into the
environmental configuration in terms of states of objects involved in the brushing task. We classify subtasks
using a Bayesian Network (BN) classifier and a Bayesian Filtering approach. We compare three variants of
the BN using different observation models (IU, NaiveBayes and Holistic) with a maximum-margin classifier
(multi-class SVM). We present recognition results on 18 trials with regular users and found the BN with a
rvation model to produce the best recognition rates of 84.5% on avg.