Publications:Detecting and exploring deviating behaviour of people in their own homes
From ISLAB/CAISR
| Title | Detecting and exploring deviating behaviour of people in their own homes |
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| Author | Jens Lundström and Eric Järpe and Antanas Verikas |
| Year | 2014 |
| PublicationType | Journal Paper |
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| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:716356 |
| Abstract | A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. It is believed that such systems will be very important in ambient assisted living services. Three types of deviations are considered in this work: deviation in activity intensity, deviation in time and deviation in space. Detection of deviations in activity intensity is formulated as the on-line quickest detection of a parameter shift in a sequence of independent Poisson random variables. Random forests trained in an unsupervised fashion are used to learn the spatial and temporal structure of data representing normal behaviour and are thereafter utilised to find deviations.The experimental investigations have shown that the Page and Shiryaev change-point detection methods are preferable in terms of expected delay of motivated alarm. Interestingly only a little is lost when the methods are specified with estimated intensity parameters rather than the true intensity values which are not available in a real situation. As to the spatial and temporal deviations, they can be revealed through analysis of a 2D map of high dimensional data. It was demonstrated that such a map is stable in terms of the number of clusters formed. We have shown that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree. |
