Next Article in Journal
Analytical Model of a Wireless Sensor Network (WSN) Node Operation with a Modified Threshold-Type Energy Saving Mechanism
Previous Article in Journal
Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks
Previous Article in Special Issue
A Technological Review of Wearable Cueing Devices Addressing Freezing of Gait in Parkinson’s Disease
Article Menu

Export Article

Open AccessArticle

Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People

1
Tecnalia, Parque Científico y Tecnológico de Gipuzkoa, Mikeletegi Pasealekua, 2. 20009 San Sebastián, Spain
2
Escuela Técnica Superior de Ingenieros Industrales, Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006 Madrid, Spain
3
i3-crg, École Politechnique, Route de Saclay, 91128 Palaiseau, France
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3113; https://doi.org/10.3390/s19143113
Received: 7 May 2019 / Revised: 3 July 2019 / Accepted: 11 July 2019 / Published: 14 July 2019
(This article belongs to the Special Issue From Sensors to Ambient Intelligence for Health and Social Care)
  |  
PDF [1694 KB, uploaded 15 July 2019]
  |  

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. View Full-Text
Keywords: smart building; IoT; machine learning; ambient intelligence; ambient assisted living smart building; IoT; machine learning; ambient intelligence; ambient assisted living
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Antón, M.Á.; Ordieres-Meré, J.; Saralegui, U.; Sun, S. Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People. Sensors 2019, 19, 3113.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top