Abstract
Currently, most smart homes are aimed at user comfort or even energy efficiency. However, there are many cases in which Ambient Assisted Living is being used, to control the health of the elderly people, or people with disabilities. In this paper, a proposal for an IoT system for activity recognition in a smart home will be shown. Specifically, various low-cost sensors are incorporated into a home that send data to the cloud. In addition, an activity recognition algorithm has been included to classify the information from the sensors and to determine which activity has been carried out. Results are also displayed in a web system, allowing the user to validate them or correct them. This web system allows the visualization of the data generated by the sensors of the smart home and help to easily monitor the activities carried out, and to alert to the doctors or the user’s family when bad habits or any problem in the behaviour are detected.
This work has been supported by the Spanish Ministry of Economy, Industry and Competitiveness under grant RTI2018-095993-B-100, and the Spanish Junta de Andalucía under grants P18-RT-1193 and UAL18-TIC-A020-B, co-funded by FEDER funds.
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
Notes
- 1.
Single Board Computer.
- 2.
Platform as a Service.
References
Ding, D., Cooper, R.A., Pasquina, P.F., Fici-Pasquina, L.: Smart homes and home health monitoring technologies for older adults: a systematic review. Int. J. Med. Inform. 91(2), 44–59 (2016)
Chana, L., Campoa, E., Estèvea, D., Fourniols, J.: Smart homes - current features and future perspectives. Maturitas 64(2), 90–97 (2019)
Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I.A.T., Siddiqa, A., Yaqoob, I.: Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access 5, 5247–5261 (2017)
Yin, J., Fang, M., Mokhtari, G., Zhang, Q.: Multi-resident location tracking in smart home through non-wearable unobtrusive sensors. LNCS, vol. 9677, pp. 3–13. Springer (2016)
Ding, D., Cooper, R.A., Pasquina, P.F., Fici-Pasquina, L.: Sensor technology for smart homes. Maturitas 69(2), 131–136 (2011)
Samarah, S., Zamil, M.G.A., Aleroud, A.F., Rawashdeh, M., Alhamid, M.F., Alamri, A.: An efficient activity recognition framework: toward privacy-sensitive health data sensing. J. Acoust. Soc. Am. 117(3), 988–988 (2017)
Chan, L., Hoey, J., Nugent, C.D., Cook, D.J., Yu, Z.: Sensor-based activity recognition. IEEE Trans. Syst. 42(6), 790–808 (2012)
Soulas, J., Lenca, P., Thépaut, A.: Unsupervised discovery of activities of daily living characterized by their periodicity and variability. Eng. Appl. Artif. Intell. 15, 90–102 (2015)
Alsheikh, M.A., Lin, S., Niyato, D., Tan, H.: Machine learning in wireless sensor networks: algorithms, strategies, and applications. IEEE Commun. Surv. Tutor. 16(4), 1996–2018 (2014)
Deng, Z., Zhu, X., Cheng, D., Zong, M., Zhang, S.: Efficient kNN classification algorithm for big data. Neurocomputing 195(3), 143–148 (2017)
Jurek, A., Nugent, C., Bi, Y., Wu, S.: Clustering-based ensemble learning for activity recognition in smart homes. J. Acoust. Soc. Am. 117(3), 988–988 (2014)
Wan, J., O’Grady, M.J., O’Hare, G.M.P.: Dynamic sensor event segmentation for real-time activity recognition in a smart home context. Pers. Ubiquit Comput. 19, 287–301 (2015)
Wang, J., Yiqiang, C., Shuji, H., Xiaohui, P., Lisha, H.: Deep learning for sensor-based activity recognition: a survey. Pattern Recogn. Lett. 119, 3–11 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lupión, M., Redondo, J.L., Sanjuan, J.F., Ortigosa, P.M. (2021). Deployment of an IoT Platform for Activity Recognition at the UAL’s Smart Home. In: Novais, P., Vercelli, G., Larriba-Pey, J.L., Herrera, F., Chamoso, P. (eds) Ambient Intelligence – Software and Applications . ISAmI 2020. Advances in Intelligent Systems and Computing, vol 1239. Springer, Cham. https://doi.org/10.1007/978-3-030-58356-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-58356-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58355-2
Online ISBN: 978-3-030-58356-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)