Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/108059
Title: A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments
Authors: Abade, Bruno 
Perez Abreu, David 
Curado, Marília 
Keywords: smart environments; Internet of Things; indoor occupancy; machine learning; data analysis
Issue Date: 15-Nov-2018
Publisher: MDPI
Project: Foundation for Science and Technology and by the European Regional Development Fund (FEDER), through the COMPETE 2020–Operational Program for Competitiveness and Internationalization (POCI) 
MobiWise project: From mobile sensing to mobility advising (P2020 SAICTPAC/0011/2015), co-financed by COMPETE 2020, Portugal 2020–Operational Program for Competitiveness and Internationalization (POCI), European Union s ERDF (European Regional Development Fund), and the Portuguese Foundation for Science and Technology (FCT) 
Serial title, monograph or event: Sensors (Switzerland)
Volume: 18
Issue: 11
Abstract: Smart Environments try to adapt their conditions focusing on the detection, localisation, and identification of people to improve their comfort. It is common to use different sensors, actuators, and analytic techniques in this kind of environments to process data from the surroundings and actuate accordingly. In this research, a solution to improve the user's experience in Smart Environments based on information obtained from indoor areas, following a non-intrusive approach, is proposed. We used Machine Learning techniques to determine occupants and estimate the number of persons in a specific indoor space. The solution proposed was tested in a real scenario using a prototype system, integrated by nodes and sensors, specifically designed and developed to gather the environmental data of interest. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. Additionally, the analysis performed over the gathered data using Machine Learning and pattern recognition mechanisms shows that it is possible to determine the occupancy of indoor environments.
URI: https://hdl.handle.net/10316/108059
ISSN: 1424-8220
DOI: 10.3390/s18113953
Rights: openAccess
Appears in Collections:FCTUC Eng.Informática - Artigos em Revistas Internacionais

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