Inferring Multi-person Presence in Home Sensor Networks
We present an evaluation of two approaches to the problem of inferring the presence of multiple persons in networks of binary sensors. This problem is critical for many applications of Ambient Assisted Living that benefit from knowledge of single- and multi-person presence where data is collected using ambient sensors. Both approaches make use of a graph representing sensors and their spatial relations. One approach uses a simple statistical method to derive a minimum number of people present, the other precisely tracks people through the sensor network. Both approaches are evaluated in a low and higher resolution setting on data of two persons inhabiting a laboratory equipped with motion sensors and contact sensors. Although the latter approach performs well tracking multiple persons, its inability to distinguish inactivity and absence make the former approach more suitable for this task, independent of resolution.
KeywordsMulti-target tracking Ambient Assisted Living
This work was partly funded by the German Ministry for Education and Research (BMBF) within the research project Cicely (grant 16SV5896). The authors would also like to thank the team of the Center for Advanced Studies in Adaptive Systems (CASAS) at the Washington State University for making their data publicly available, and Prof. Aaron Crandall for the permission to reuse Fig. 2.
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