STCMS: A Smart Thermal Comfort Monitor For Senior People

Katsarou, Katerina; Ounoughi, Chahinez; Mouakher, Amira; Nicolle, Christophe

doi:10.1109/wetice49692.2020.00044

Cited by 1 publication

(3 citation statements)

References 12 publications

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“…For the development of this article, big data solutions were considered to support the continuous transmission of data and the use of AutoML to streamline and simplify the analysis, processing and predictions of the environment parameters, according to the suggestions of future works by Sergi I, Catarinucci L, Montanaro T, et al 20 ; Ray PP 21 ; and Katsarou K, Ounoughi C, Mouakher A, et al 28…”

Section: Methodsmentioning

confidence: 99%

“…The system determines environmental conditions without using user feedback on environmental conditions. 27 For the development of this article, big data solutions were considered to support the continuous transmission of data and the use of AutoML to streamline and simplify the analysis, processing and predictions of the environment parameters, according to the suggestions of future works by Sergi I, Catarinucci L, Montanaro T, et al 20 ; Ray PP 21 ; and Katsarou K, Ounoughi C, Mouakher A, et al 28 For data collection regarding air temperature (°C) and relative air humidity (%), IoT motes were used. Each one, illustrated in Figure 1, is composed of a DOIT ESP32 -ESP-WROOM-32 board connected to a Wi-Fi network, a DHT11 temperature and humidity sensor.…”

Section: Methodsmentioning

confidence: 99%

“…The authors identified that there is a need to use alternatives to model the comfort of occupants without using periodic user feedback. Katsarou K, Ounoughi C, Mouakher A, et al 28 have introduced a new product to the market, a new deep learning-based model framework called STCMS, which uses long short-term memory (LSTM) and neural networks architectures deep (DNN), dedicated to predicting thermal comfort for elderly people. The elderly population spends up to 90% of their time indoors.…”

Section: Introductionmentioning

confidence: 99%

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Internet of things and machine learning applied to the thermal comfort of internal environments

Nascimento

Lopes

2022

Indoor and Built Environment

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The internet of things connects objects to the internet, enabling the dialogue between devices and users, providing new opportunities for applications, such as thermal comfort. In the research, adequate sensors were used to measure the heat index, the thermal discomfort index and the temperature and humidity index based on the temperature and relative humidity of a remote indoor environment. This research evaluated the level of thermal comfort in real-time using tools of storage, processing and analysis of big data information from the collection of IoT devices. With the analysis of the environment, it is possible to intelligently monitor the level of comfort and alert possible hazards to the people present. Machine learning algorithms were also used to analyse the history of stored data and formulate models capable of making predictions of the parameters of the environment. Health researchers, for example, have the necessary knowledge to evaluate clinical data, but they are not used to using data analysis resources and machine learning algorithms. The platform was developed to reduce dependence on data experts and encourage healthcare researchers to develop their own models by automating the steps required for model development, using automated machine learning (AutoML).

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Section: Methodsmentioning

confidence: 99%