Abstract
Activity recognition systems are composed of devices with sensors which, through artificial intelligence techniques, can detect activities performed by people in their homes. Many of these systems are deployed in multi-occupancy environments, so indoor localization approaches are combined with activity recognition systems to achieve discrimination of activities in the same space. The benefits of these systems are numerous such as remote monitoring for anomaly warning or improved safety of the monitored person. Although there is an extensive study on these systems from the technical point of view, there is an important gap in the literature on their energy consumption. This fact is even more relevant considering that one of the most important concerns in society is the prices of electricity and it has had a great variability, with increases, due to the pandemic and the war in Ukraine. This work aims to address this scientific gap through the energy evaluation of a home activity recognition system in two scenarios. First, an ambient intelligence apartment (Smart Lab) of the University of Jaén and, then, a single-family house. The evaluation carried out provides quantitative data considering the data of the year 2022, with a price between 3.125€and 4.018€and qualitative data from the point of view of patients, healthcare professionals and researchers.
Grant PID2021-127275OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”.
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Lendínez, A.M., Ruiz, J.L.L., Jiménez, D.D., Estévez, M.E., Nugent, C. (2023). Shedding Light on the Energy Usage of Activity Recognition Systems in Homes. In: Bravo, J., Urzáiz, G. (eds) Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023). UCAmI 2023. Lecture Notes in Networks and Systems, vol 841. Springer, Cham. https://doi.org/10.1007/978-3-031-48590-9_8
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