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.
Original language | English |
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Article number | 3113 |
Pages (from-to) | 3113 |
Number of pages | 1 |
Journal | Sensors |
Volume | 19 |
Issue number | 14 |
DOIs | |
Publication status | Published - 2 Jul 2019 |
Keywords
- Smart building
- IoT
- Machine learning
- Ambient intelligence
- Ambient assisted living
Project and Funding Information
- Funding Info
- This research was partially funded by Fundación Tecnalia Research & Innovation, and J.O.-M. also wants_x000D_ to recognise the support obtained from the EU RFCS program through project number 793505 ‘4.0 Lean system_x000D_ integrating workers and processes (WISEST)’ and from the grant PRX18/00036 given by the Spanish Secretaría_x000D_ de Estado de Universidades, Investigación, Desarrollo e Innovación del Ministerio de Ciencia, Innovación_x000D_ y Universidades.