期刊
SENSORS
卷 18, 期 11, 页码 -出版社
MDPI
DOI: 10.3390/s18113953
关键词
smart environments; Internet of Things; indoor occupancy; machine learning; data analysis
资金
- Foundation for Science and Technology
- European Regional Development Fund (FEDER), through the COMPETE 2020-Operational Program for Competitiveness and Internationalization (POCI)
- European Union s ERDF (European Regional Development Fund) [P2020 SAICTPAC/0011/2015]
- Portuguese Foundation for Science and Technology (FCT) [P2020 SAICTPAC/0011/2015]
- Portugal 2020-Operational Program for Competitiveness and Internationalization (POCI) [P2020 SAICTPAC/0011/2015]
- COMPETE 2020 [P2020 SAICTPAC/0011/2015]
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.
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