Abstract
Smart homes offer considerable potential to facilitate aging at home and, therefore, to reduce healthcare costs, both in financial and human resources. To implement the smart home dream, an artificial intelligence has to be able to identify, in real-time, the ongoing activity of daily living with a fine-grained granularity. Despite the recent and ongoing improvements, the limitation of the literature on this subject primarily concerns the quality of the information which can be inferred from standard ubiquitous sensors in a smart home. Passive Radio-Frequency Identification is one of the technology that can help improving activity recognition through the tracking of the objects used by the resident in real-time. This paper builds upon the literature on objects tracking to propose a machine learning scheme exploiting statistical features to transform the signal strength into useful qualitative spatial information. The method has an overall accuracy of 95.98%, which is an improvement of 8.26% over previous work.
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References
Nations, U.: World Population Ageing. Department of Economic and Social Affairs, pp. 1–164 (2015)
Moatamed, B., et al.: Low-cost indoor health monitoring system. In: 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE (2016)
Al-Shaqi, R., Mourshed, M., Rezgui, Y.: Progress in ambient assisted systems for independent living by the elderly. SpringerPlus 5(1), 624 (2016)
Cook, D.J., et al.: CASAS: a smart home in a box. Computer 46(7), 62–69 (2013)
Hsu, Y.-L., et al.: Design and implementation of a smart home system using multisensor data fusion technology. Sensors 17(7), 1631 (2017)
Bouchard, K., Bouchard, B., Bouzouane, A.: Guideline to efficient smart home design for rapid AI prototyping: a case study. In: International Conference on PErvasive Technologies Related to Assistive Environments, Crete Island, Greece. ACM (2012)
Krishnan, N.C., Cook, D.J.: Activity recognition on streaming sensor data. Pervasive Mob. Comput. 10, 138–154 (2014)
Belley, C., et al.: Efficient and inexpensive method for activity recognition within a smart home based on load signatures of appliances. J. Pervasive Mob. Comput. 12, 1–20 (2013)
Fortin-Simard, D., et al.: Exploiting passive RFID technology for activity recognition in smart homes. IEEE Intell. Syst. 30(4), 7–15 (2015)
Bergeron, F., Bouchard, K., Gaboury, S., Giroux, S., Bouchard, B.: Indoor positioning system for smart homes based on decision trees and passive RFID. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J.Z., Wang, R. (eds.) PAKDD 2016. LNCS (LNAI), vol. 9652, pp. 42–53. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31750-2_4
Huynh, S.M., et al.: Novel RFID and ontology based home localization system for misplaced objects. IEEE Trans. Consum. Electron. 60(3), 402–410 (2014)
Hekimian-Williams, C., Grant, B., Kumar, P.: Accurate localization of RFID tags using phase difference. In: 2010 IEEE International Conference on RFID IEEE RFID 2010, pp. 89–96 (2010)
Dulimart, H.S., Jain, A.K.: Mobile robot localization in indoor environment. Pattern Recogn. 30(1), 99–111 (1997)
Liu, H., et al.: Survey of wireless indoor positioning techniques and systems. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 37(6), 1067–1080 (2007)
Song, J., Haas, C.T., Caldas, C.H.: A proximity-based method for locating RFID tagged objects. Adv. Eng. Inform. 21(4), 367–376 (2007)
Ni, L.M., et al.: LANDMARC: indoor location sensing using active RFID. ACM Wirel. Netw. 10(6), 701–710 (2004)
Faragher, R., Harle, R.: Location fingerprinting with bluetooth low energy beacons. IEEE J. Sel. Areas Commun. 33(11), 2418–2428 (2015)
Hightower, J., Borriello, G.: Location systems for ubiquitous computing. Computer 34(8), 57–66 (2001)
Bouchard, K., Bouchard, B., Bouzouane, A.: Spatial recognition of activities for cognitive assistance: realistic scenarios using clinical data from Alzheimer’s patients. J. Ambient Intell. Humaniz. Comput. 5(5), 759–774 (2014)
Bouchard, K., et al.: Accurate trilateration for passive RFID localization in smart homes. Int. J. Wirel. Inf. Netw. 21(1), 32–47 (2014)
Joanes, D., Gill, C.: Comparing measures of sample skewness and kurtosis. J. R. Stat. Soc.: Series D (Stat.) 47(1), 183–189 (1998)
Hall, M., et al.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
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This project success is the direct consequence of the financial support received from the Université du Québec à Chicoutimi and the National Sciences and Engineering Research Council of Canada.
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Bouchard, K. (2018). Statistical Features for Objects Localization with Passive RFID in Smart Homes. In: Guidi, B., Ricci, L., Calafate, C., Gaggi, O., Marquez-Barja, J. (eds) Smart Objects and Technologies for Social Good. GOODTECHS 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 233. Springer, Cham. https://doi.org/10.1007/978-3-319-76111-4_3
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DOI: https://doi.org/10.1007/978-3-319-76111-4_3
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