Abstract
This paper presents a rule-based approach for both offline and real-time recognition of Activities of Daily Living (ADL), leveraging events produced by a non-intrusive multi-modal sensor infrastructure deployed in a residential environment. Novel aspects of the approach include: the ability to recognise arbitrary scenarios of complex activities using bottom-up multi-level reasoning, starting from sensor events at the lowest level; an effective heuristics-based method for distinguishing between actual and ghost images in video data; and a highly accurate indoor localisation approach that fuses different sources of location information. The proposed approach is implemented as a rule-based system using Jess and is evaluated using data collected in a smart home environment. Experimental results show high levels of accuracy and performance, proving the effectiveness of the approach in real world setups.
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This work was performed under the SPHERE IRC, funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant EP/K031910/1.
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Baryannis, G., Woznowski, P., Antoniou, G. (2016). Rule-Based Real-Time ADL Recognition in a Smart Home Environment. In: Alferes, J., Bertossi, L., Governatori, G., Fodor, P., Roman, D. (eds) Rule Technologies. Research, Tools, and Applications. RuleML 2016. Lecture Notes in Computer Science(), vol 9718. Springer, Cham. https://doi.org/10.1007/978-3-319-42019-6_21
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DOI: https://doi.org/10.1007/978-3-319-42019-6_21
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