Abstract
The aging of population in recent years and the increase in life expectancy is raising challenges for finding new ways to guarantee healthy and controlled activities for the elderly. Most of them prefer living in their houses than in a community center, even if they live alone or isolated from their family; at home, their normal routine activities and comfort makes them feel well. In this paper, an Active and Assisted Living (AAL) solution to detect irregular situations in everyday life of the elderly living alone is presented. By using low-cost sensors in an Internet of Things (IoT) architecture we aim to gather data in specific areas of an elderly’s house in order to give the system enough input to detect abnormal behavior. These sensors are non-intrusive to the elderly, do not disturb them, and do not force them to wear a device at all times. These sensors can also send information to edge computing devices that analyze the data in real time using machine learning algorithms and alert family or caretakers when an unusual situation arises. The proposed solution provides a system that monitors the main activities performed by the elderly and creates patterns based on that activity to achieve its results and is scalable in terms of sensors and data input.
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This publication is funded by FCT - Fundação para a Ciência e Tecnologia, I.P., under the projects identified by UID/CEC/04524/2016 and UID/CEC/04524/2019.
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Almeida, A.H. et al. (2020). Real-Time Low-Cost Active and Assisted Living for the Elderly. In: Novais, P., Lloret, J., Chamoso, P., Carneiro, D., Navarro, E., Omatu, S. (eds) Ambient Intelligence – Software and Applications –,10th International Symposium on Ambient Intelligence. ISAmI 2019. Advances in Intelligent Systems and Computing, vol 1006 . Springer, Cham. https://doi.org/10.1007/978-3-030-24097-4_19
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DOI: https://doi.org/10.1007/978-3-030-24097-4_19
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