Abstract
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.
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To save energy, and due to radio communication regulations, most home automation sensors have a downtime that causes them to stop measuring and sending new data after being triggered. From our experience, depending on the sensor, this downtime lasts between 8 s and 10 min.
References
Blackman, S.S.: Multiple hypothesis tracking for multiple target tracking. Aerosp. Electron. Syst. Mag. IEEE 19(1), 5–18 (2004)
Crandall, A.S., Cook, D.J.: Tracking systems for multiple smart home residents. Human Behavior Recognition Technologies (2010)
Frenken, T., Steen, E.-E., Brell, M., Nebel, W., Hein, A.: Motion pattern generation and recognition for mobility assessments in domestic environments. In: Proceedings of the 1st International Living Usability Lab Workshop on AAL Latest Solutions, pp. 3–12 (2011)
Krüger, F., Kasparick, M., Mundt, T., Kirste T.: Where are my colleagues and why? tracking multiple persons in indoor environments. In: Proceedings of the International Conference on Intelligent Environments, pp. 190–197. IEEE (2014)
Mori, T., Suemasu, Y., Noguchi, H., Sato, T.: Multiple people tracking by integrating distributed floor pressure sensors and rfid system. In: Proceedings of the International Conference on Systems, Man and Cybernetics, vol. 6, pp. 5271–5278. IEEE (2004)
Müller, S.M., Hein, A.: Multi-target tracking in home sensor networks. In: Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments, Prague, Czech Republic, August 2014. CEUR-WS.org. To appear
Müller, S., Helmer, A., Steen, E.-E., Frenken, M., Hein, A.: Automated clustering of home sensor networks to functional regions for the deduction of presence information for medical applications. Wohnen-Pflege-Teilhabe-Besser leben durch Technik (2014)
Reid, D.B.: An algorithm for tracking multiple targets. IEEE Trans. Autom. Control 24(6), 843–854 (1979)
Sixsmith, A., Johnson, N., Whatmore, R.: Pyroelectric ir sensor arrays for fall detection in the older population. Journal de Physique IV (Proceedings) 128, 153–160 (2005). EDP sciences
Teixeira, T., Savvides, A.: Lightweight people counting and localizing for easily deployable indoors wsns. IEEE J. Sel. Top. Signal Process. 2(4), 493–502 (2008)
Acknowledgments
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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Müller, S.M., Steen, EE., Hein, A. (2016). Inferring Multi-person Presence in Home Sensor Networks. In: Wichert, R., Klausing, H. (eds) Ambient Assisted Living. Advanced Technologies and Societal Change. Springer, Cham. https://doi.org/10.1007/978-3-319-26345-8_5
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DOI: https://doi.org/10.1007/978-3-319-26345-8_5
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