Abstract
Active Aging is a proposal that aims to improve life quality, as a person grows old. One of the main use cases of this concept is the application of products and services based on technology; this approach, known as Ambient Assisted Living (AAL). An important activity performed by AAL is the discovery of the user’s activities of daily life (ADL) employing data retrieved from sensors set on an active home. Still, there is no much research on implementing a system for ADL discovery which contemplates factors as personalized configuration, sensor failure and user privacy. We identify the main requirements that an ADL discovery system must have. Then, we propose an ADL discovery schema that supports these necessities. Finally, we explore the application of adaptable and sensor-failure tolerant ADL discovery models over recorded data from a real user. This exploration evidences that our proposed models can adapt to the above-mentioned scenarios and still have an outstanding performance on activity discovery process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Palacios, R.: The future of global ageing. Int. J. Epidemiol. 31, 786–791 (2002)
Bloom, D.E., Boersch-Supan, A., McGee, P., Seike, A.: Population aging: facts, challenges, and responses. PGDA Work. Pap., no. 71 (2011)
Org, S.G., et al.: Envejecimiento activo: un marco político*, vol. 37, pp. 74–105 (2002)
Powell, J.L., Chen, S.: Technologies for Active Aging. Springer, London (2013)
World Health Organization, Noncommunicable diseases (2017). http://www.who.int/mediacentre/factsheets/fs355/en/. Accessed 19 May 2017
Chalmers, D.: Sensing and Systems in Pervasive Computing, 1st edn. Springer, London (2011)
Lago, P., Jiménez-Guarín, C., Roncancio, C.: A case study on the analysis of behavior patterns and pattern changes in smart environments. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds.) IWAAL 2014. LNCS, vol. 8868, pp. 296–303. Springer, Cham (2014). doi:10.1007/978-3-319-13105-4_43
Lark, Lark - About Us. http://www.web.lark.com/about/. Accessed 19 May 2017
Imai, S., Galli, A., Varela, C.A.: Dynamic data-driven avionics systems: inferring failure modes from data streams. Procedia Comput. Sci. 51, 1665–1674 (2015)
INRIA, About - Amiqual4home. https://amiqual4home.inria.fr/home/. Accessed: 19 May 2017
Lago, P., Lang, F., Roncancio, C., Jiménez-Guarín, C., Mateescu, R., Bonnefond, N.: The ContextAct@A4H real-life dataset of daily-living activities. In: Brézillon, P., Turner, R., Penco, C. (eds.) CONTEXT 2017. LNCS, vol. 10257, pp. 175–188. Springer, Cham (2017). doi:10.1007/978-3-319-57837-8_14
Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proceedings of the Seventh ACM SIGKDD International Conference Knowledge Discovery and Data Mining, KDD 2001, vol. 18, pp. 97–106 (2001)
Urwyler, P., et al.: Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers. Biomed. Eng. Online, 1–15 (2015)
Duque, A., Ordóñez, F.J., Toledo, P., Sanchis, A.: Offline and online activity recognition on mobile devices using accelerometer data. In: Bravo, J., Hervás, R., Rodríguez, M. (eds.) IWAAL 2012. LNCS, vol. 7657, pp. 208–215. Springer, Heidelberg (2012). doi:10.1007/978-3-642-35395-6_29
Chen, L., Nugent, C., Wang, H.: A knowledge-driven approach to activity recognition in smart homes. IEEE Trans. Knowl. Data Eng. 24(6), 961–974 (2012)
Dimitrievski, A., Zdravevski, E., Lameski, P., Trajkovik, V.: Towards application of non-invasive environmental sensors for risks and activity detection. In: Proceedings of the 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing, ICCP 2016, pp. 27–33 (2016)
Amirjavid, F., Spachos, P., Plataniotis, K.N.: 3-D object localization in smart homes: a distributed sensor and video mining approach, pp. 1–10 (2017)
Lun, R., Gordon, C., Zhao, W.: Tracking the activities of daily lives: an integrated approach. In: Proceedings of the Future Technologies Conference, FTC 2016, December 2017
Negin, F., Cosar, S., Koperski, M., Bremond, F.: Generating unsupervised models for online long-term activity recognition. In: 3rd IAPR Asian Conference on Pattern Recognition (2015)
Acknowledgments
This research was sponsored and supported by Alianza Caoba (Centro de Excelencia en Big Data y Data Analytics, Colombia). We thank our colleagues Claudia Roncancio, and Cyril Labbé from Universite Grenoble Alpes, LIG; and Paula Lago from Universidad de los Andes, who collaborate in our research project, provided expertise on the subjects under discussion and provided the datasets for the corresponding analysis.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Molano-Pulido, J., Jiménez-Guarín, C. (2017). SEAbIRD: Sensor Activity Identification from Streams of Data. In: Solano, A., Ordoñez, H. (eds) Advances in Computing. CCC 2017. Communications in Computer and Information Science, vol 735. Springer, Cham. https://doi.org/10.1007/978-3-319-66562-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-66562-7_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66561-0
Online ISBN: 978-3-319-66562-7
eBook Packages: Computer ScienceComputer Science (R0)