Publications:A Holistic Smart Home Demonstrator for Anomaly Detection and Response
From ISLAB/CAISR
| Title | A Holistic Smart Home Demonstrator for Anomaly Detection and Response |
|---|---|
| Author | Jens Lundström and Wagner Ourique de Morais and Martin Cooney |
| Year | 2015 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) |
| Conference | SmartE: Closing the Loop – The 2nd IEEE PerCom Workshop on Smart Environments, St. Louis, Missouri, USA, March 23-27, 2015 |
| DOI | http://dx.doi.org/10.1109/PERCOMW.2015.7134058 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:814235 |
| Abstract | Applying machine learning methods in scenarios involving smart homes is a complex task. The many possible variations of sensors, feature representations, machine learning algorithms, middle-ware architectures, reasoning/decision schemes, and interactive strategies make research and development tasks non-trivial to solve.In this paper, the use of a portable, flexible and holistic smart home demonstrator is proposed to facilitate iterative development and the acquisition of feedback when testing in regard to the above-mentioned issues. Specifically, the focus in this paper is on scenarios involving anomaly detection and response. First a model for anomaly detection is trained with simulated data representing a priori knowledge pertaining to a person living in an apartment. Then a reasoning mechanism uses the trained model to infer and plan a reaction to deviating activities. Reactions are carried out by a mobile interactive robot to investigate if a detected anomaly constitutes a true emergency. The implemented demonstrator was able to detect and respond properly in 18 of 20 trials featuring normal and deviating activity patterns, suggesting the feasibility of the proposed approach for such scenarios. © IEEE 2015 |
