Movement Recognition using Context: a Lexical Approach Based on Coherence

Movement recognition constitutes a central task in homebased assisted living environments and in many application domains where activity recognition is crucial. Solutions in these application areas often rely on an heterogeneous collection of body-sensors whose diversity and lack of precision has to be compensated by advanced techniques for feature extraction and analysis. Although there are well established quantitative methods in machine learning for robotics and neighboring fields for addressing these problems, they lack advanced knowledge representation and reasoning capacities that may help understanding through contextualization. Such capabilities are not only useful in dealing with lacking and imprecise information, but moreover they allow for a better inclusion of semantic information and more general domain-related knowledge. We address this problem and investigate how a lexical approach to multisensor analysis can be combined with answer set programming to support movement recognition. A semantic notion of contextual coherence is formalized and qualitative optimization criteria are introduced in the reasoning process. We report upon a first experimental evaluation of the lexical approach to multi-sensor analysis and discuss the potentials of knowledge-based contextualization of movements in reducing the error rate.