Abstract
With the increase in presence of smart devices in our daily life, it is an important problem for these devices to be more intelligent. The most sought after problems in this area are activity recommendation and prediction. Researchers have proposed solutions for this problem, however, most of them are based on single-user home space. In this paper, we propose an unsupervised approach to separate the logs of multi-user home space into buckets equal to the number of users. With a minimal set of assumptions, the aim of the method is to transform the multi-user problem to a single-user problem. It is achieved by estimating the layout of the house and then tracking the users at room-level. We achieved empirically-determined high precision in estimating the layout and 74% accuracy in separating the multi-user stream.
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Notes
- 1.
Can be accessed using the URL: http://casas.wsu.edu/datasets/.
References
Cook, D., Das, S.K.: Smart Environments: Technology, Protocols and Applications. John Wiley & Sons, 28 October 2004
Wu, Z.H., Liu, A., Zhou, P.C., Su, Y.F.: A Bayesian network based method for activity prediction in a smart home system. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 001496–001501. IEEE, 9 October 2016
Nazerfard, E., Cook, D.J.: CRAFFT: an activity prediction model based on Bayesian networks. J. Ambient Intell. Humaniz. Comput. 6(2), 193–205 (2015)
Gopalratnam, K., Cook, D.J.: Active lezi: an incremental parsing algorithm for sequential prediction. Int. J. Artif. Intell. Tools 13(04), 917–29 (2004)
Alam, M.R., Reaz, M.B., Ali, M.M.: SPEED: an inhabitant activity prediction algorithm for smart homes. IEEE Trans. Syst. Man Cybern.-Part A Syst. Hum. 42(4), 985–90 (2012)
Kim, Y., An, J., Lee, M., Lee, Y.: An activity-embedding approach for next-activity prediction in a multi-user smart space. In: 2017 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1–6. IEEE, 29 May 2017
Moraru, A., Pesko, M., Porcius, M., Fortuna, C., Mladenic, D.: Using machine learning on sensor data. J. Comput. Inf. Technol. 18(4), 341–347 (2010)
Yang, L., Ting, K., Srivastava, M.B.: Inferring occupancy from opportunistically available sensor data. In: 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 60–68. IEEE, 24 March 2014
Fruchterman, T.M., Reingold, E.M.: Graph drawing by force-directed placement. Soft. Pract. Exp. 21(11), 1129–1164 (1991)
Cook, D.J., Crandall, A.S., Thomas, B.L., Krishnan, N.C.: CASAS: a smart home in a box. Computer 46(7), 62–69 (2013)
Bourobou, S.T., Yoo, Y.: User activity recognition in smart homes using pattern clustering applied to temporal ANN algorithm. Sensors 15(5), 11953–11971 (2015)
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Ranka, S., Singh, V., Choudhury, M. (2018). USHEr: User Separation in Home Environment. In: Mokhtari, M., Abdulrazak, B., Aloulou, H. (eds) Smart Homes and Health Telematics, Designing a Better Future: Urban Assisted Living. ICOST 2018. Lecture Notes in Computer Science(), vol 10898. Springer, Cham. https://doi.org/10.1007/978-3-319-94523-1_19
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DOI: https://doi.org/10.1007/978-3-319-94523-1_19
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