Abstract
This 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.
Highlights
It has been largely demonstrated that huge improvements in human health and the increased attention being paid to the impacts of different factors (e.g., food and habits)on quality of life are resulting in the increased longevity of humans
“ambient assisted living” (AAL) aiming to help individuals to continue to carry out their daily activities in their homes, for as long as possible and with the least amount of assistance
There are research works aimed at tracking the movements of individuals with high precision, in order to gain knowledge regarding how the interactions develop for different tasks, as a wide variety of Internet of Things (IoT) devices are used [2,3], this paper aims to discuss how to exploit the advantages of the knowledge gathered in much more real contexts with non-intrusive sensors
Summary
It has been largely demonstrated that huge improvements in human health and the increased attention being paid to the impacts of different factors (e.g., food and habits (smoking, sports, etc.)). On quality of life are resulting in the increased longevity of humans. This effect will dramatically impact the required infrastructures (e.g., homes). There are different approaches for handling this, starting from ambient intelligence (AmI), which refers to electronic environments that are sensitive and responsive to the presence of people. It is possible to identify “ambient assisted living” (AAL) aiming to help individuals to continue to carry out their daily activities in their homes, for as long as possible and with the least amount of assistance
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