Abstract
Behavior recognition through ambient assisted living solutions for elderly people represents an ambitious challenge for actimetry. Numerous and versatile solutions have been deployed. However, a commercial adoption is still pending, due to scalability and acceptability constraints. Most research in Ambient Assisted Living (AAL) appears to have a heavy design, where precise features are first selected, and hardware architecture is designed accordingly. Although it may provide interesting results, such approach leads to a lack of scalability. This is why we experimented a lighter approach for a real deployment. The complexity is shifted from hardware to software, and we aim to make meaningful information emerge from simple and generic sensor data, in order to recognize abnormal and dangerous situations. In this paper, we will describe how to retrieve consistent information, so that residents’ behaviors may be observed. This work might serve as a proof of concept that a light and generic approach fits in large scale deployments, with acceptable cost and scalability.
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Endelin, R., Renouard, S., Tiberghien, T., Aloulou, H., Mokhtari, M. (2013). Behavior Recognition for Elderly People in Large-Scale Deployment. In: Biswas, J., Kobayashi, H., Wong, L., Abdulrazak, B., Mokhtari, M. (eds) Inclusive Society: Health and Wellbeing in the Community, and Care at Home. ICOST 2013. Lecture Notes in Computer Science, vol 7910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39470-6_8
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DOI: https://doi.org/10.1007/978-3-642-39470-6_8
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