SEAbIRD: Sensor Activity Identification from Streams of Data

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 735)


Active Aging is a proposal that aims to improve life quality, as a person grows old. One of the main use cases of this concept is the application of products and services based on technology; this approach, known as Ambient Assisted Living (AAL). An important activity performed by AAL is the discovery of the user’s activities of daily life (ADL) employing data retrieved from sensors set on an active home. Still, there is no much research on implementing a system for ADL discovery which contemplates factors as personalized configuration, sensor failure and user privacy. We identify the main requirements that an ADL discovery system must have. Then, we propose an ADL discovery schema that supports these necessities. Finally, we explore the application of adaptable and sensor-failure tolerant ADL discovery models over recorded data from a real user. This exploration evidences that our proposed models can adapt to the above-mentioned scenarios and still have an outstanding performance on activity discovery process.


Ambient Assisted Living (AAL) Sensor data analysis Data stream mining 



This research was sponsored and supported by Alianza Caoba (Centro de Excelencia en Big Data y Data Analytics, Colombia). We thank our colleagues Claudia Roncancio, and Cyril Labbé from Universite Grenoble Alpes, LIG; and Paula Lago from Universidad de los Andes, who collaborate in our research project, provided expertise on the subjects under discussion and provided the datasets for the corresponding analysis.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computing and Systems Engineering Department, School of EngineeringUniversidad de los Andes, Bogotá, ColombiaBogotáColombia

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