Abstract
In this paper, we investigate the problem of recognizing multiuser activities using wearable devices in a home environment. Our research objective is to provide situation awareness so that a smart home can respond to the needs of its residents based on the accurate detection of their activities. In this research, we compare applying artificial neural network, decision tree and simple logistic regression for model construction and activity detection. Moreover, we also evaluate different architectural alternatives of our smart home system in order to discover the best system configuration. Our unique contribution lies on the low cost of the proposed system design.
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Acknowledgment
The research in this paper is funded by Ministry of Science and Technology (MOST) of Taiwan Government under Project Number MOST 105-2218-E-009-004.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Lee, Y., Lin, F.J., Chen, WH. (2018). Multiple User Activities Recognition in Smart Home. In: Lin, YB., Deng, DJ., You, I., Lin, CC. (eds) IoT as a Service. IoTaaS 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-030-00410-1_24
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DOI: https://doi.org/10.1007/978-3-030-00410-1_24
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