Spatial Information Extraction Using Hidden Correlations

To promote independent living and elderly care in a smart living space, monitoring the activities of daily living and detecting upcoming critical situations are crucial. Thus, exploiting the spatial aspect related to the movement of the resident and their relations to a specific region is important. However, most of existing solutions do not consider related spatial aspect or it is incorporated in a very limited way, in which most solutions need to know the deployment location of sensors. To achieve this, a very time consuming sensor labeling process is needed. In this paper, an unsupervised spatial information extraction technique for smart living space is developed. This technique establishes an abstract spatial representation called virtual location for a physical living space without knowing the actual deployment location of the sensors in that space. A virtual location is achieved by utilizing our extended Louvain modularity method to extract key spatial features from the hidden correlations in the observations perceived through the sensors. By considering the characteristics of different sensors, the effectiveness of virtual location can be further improved. Two real world datasets are used to validate the applicability of the propose technique, and the experimental results for them achieve 90.3% and 71.7% V-measure values respectively.