Browsing by Author "Ordieres-Meré, Joaquín"
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Item An IoT−based system that aids learning from human behavior: A potential application for the care of the elderly: A potential application for the care of the elderly(2017-10-04) Saralegui, Unai; Antón, Miguel Ángel; Ordieres-Meré, Joaquín; Tecnalia Research & Innovation; DIGITALIZACIÓN Y AUTOMATIZACIÓN DE LA CONSTRUCCIÓNThe goal of this paper is to describe the way of taking advantage of the non-intrusive indoor air quality monitoring system by using data oriented modeling technologies to determine specific human behaviors. The specific goal is to determine when a human presence occurs in a specific room, while the objective is to extend the use of the existing indoor air quality monitoring system to provide a higher level aspect of the house usage. Different models have been trained by means of machine learning algorithms using the available temperature, relative humidity and CO2 levels to determine binary occupation. The paper will discuss the overall acceptable quality provided by those classifiers when operating over new data not previously seen. Therefore, a recommendation on how to proceed is provided, as well as the confidence level regarding the new created knowledge. Such knowledge could bring additional opportunities in the care of the elderly for specific diseases that are usually accompanied by changes in patterns of behavior.Item Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People: Dealing with noisy patterns to help older people(2019-07-02) Antón, Miguel Ángel; Ordieres-Meré, Joaquín; Saralegui, Unai; Sun, Shengjing; DIGITALIZACIÓN Y AUTOMATIZACIÓN DE LA CONSTRUCCIÓN; Tecnalia Research & InnovationThis paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context. The contribution also aims to analyse the usage of the models for the transfer of knowledge when different sensors with different settings contribute to the noise levels. Finally, based on the authors’ experience, a framework proposal for creating valuable and aggregated knowledge is depicted.