Application of IoT and Machine Learning techniques for the assessment of thermal comfort perception.

Salamone, Francesco; Belussi, Lorenzo; Currò, Cristian; Danza, Ludovico; Ghellere, Matteo; Guazzi, Giulia; Lenzi, B.; Megale, Valentino; Meroni, Italo

doi:10.1016/j.egypro.2018.08.130

Cited by 25 publications

(10 citation statements)

References 15 publications

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“…An example is a study of Salamone et al [57], which based the Sensing Layer on the use of both wearable and the so-called nearable devices, a word coined to indicate sensors that refer to a micro-environment, like a single workspace. They chose wristbands as wearable devices and low-cost sensors located at 40 cm from the participants as nearable devices.…”

Section: Studies With Monitoring Purpose Using Iot Technologiesmentioning

confidence: 99%

A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?

Tomat

Ramallo-González

Skarmeta-Gómez

2020

Sensors

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This paper presents a review of technologies under the paradigm 4.0 applied to the study of the thermal comfort and, implicitly, energy efficiency. The research is based on the analysis of the Internet of Things (IoT) literature, presenting a comparison among several approaches adopted. The central objective of the research is to outline the path that has been taken throughout the last decade towards a people-centric approach, discussing how users switched from being passive receivers of IoT services to being an active part of it. Basing on existing studies, authors performed what was a necessary and unprecedented grouping of the IoT applications to the thermal comfort into three categories: the thermal comfort studies with IoT hardware, in which the approach focuses on physical devices, the mimicking of IoT sensors and comfort using Building Simulation Models, based on the dynamic modelling of the thermal comfort through IoT systems, and Crowdsensing, a new concept in which people can express their sensation proactively using IoT devices. Analysing the trends of the three categories, the results showed that Crowdsensing has a promising future in the investigation through the IoT, although some technical steps forward are needed to achieve a satisfactory application to the thermal comfort matter.

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Section: Studies With Monitoring Purpose Using Iot Technologiesmentioning

confidence: 99%

A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?

Tomat

Ramallo-González

Skarmeta-Gómez

2020

Sensors

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“…Two [ 26 , 27 ] were excluded following EC4 as they were review papers or theoretical analysis (EC3, EC4). Furthermore, 20 studies [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ] were removed due to missing information about the design methodology and the type of sensors used for measuring IAQ parameters (IC4, EC3). In addition, five more studies [ 48 , 49 , 50 , 51 , 52 ] were excluded as they were focused on thermal comfort parameters only or had no relevant details about IAQ sensors (IC2, IC3, EC3).…”

Section: Methodsmentioning

confidence: 99%

Indoor Air Quality Monitoring Systems Based on Internet of Things: A Systematic Review

Saini

Dutta

Marques

2020

IJERPH

129

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Indoor air quality has been a matter of concern for the international scientific community. Public health experts, environmental governances, and industry experts are working to improve the overall health, comfort, and well-being of building occupants. Repeated exposure to pollutants in indoor environments is reported as one of the potential causes of several chronic health problems such as lung cancer, cardiovascular disease, and respiratory infections. Moreover, smart cities projects are promoting the use of real-time monitoring systems to detect unfavorable scenarios for enhanced living environments. The main objective of this work is to present a systematic review of the current state of the art on indoor air quality monitoring systems based on the Internet of Things. The document highlights design aspects for monitoring systems, including sensor types, microcontrollers, architecture, and connectivity along with implementation issues of the studies published in the previous five years (2015–2020). The main contribution of this paper is to present the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. The results show that 70%, 65%, and 27.5% of studies focused on monitoring thermal comfort parameters, CO2, and PM levels, respectively. Additionally, there are 37.5% and 35% of systems based on Arduino and Raspberry Pi controllers. Only 22.5% of studies followed the calibration approach before system implementation, and 72.5% of systems claim energy efficiency.

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“…The research conducted in real offices involving eight workers detects human and environmental variables with IoT solutions. In [17], ML techniques are applied to identify the most relevant parameters for users' recognition. In [18], the Bagging model shows higher accuracy than Support Vector Machine (SVM) and Artificial Neural Network (ANN) in thermal perception prediction.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Visual Stimuli on Personal Thermal Comfort Perception in Real and Virtual Environments Using Machine Learning Approaches

Salamone

Bellazzi

Belussi

et al. 2020

Sensors

Self Cite

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Personal Thermal Comfort models consider personal user feedback as a target value. The growing development of integrated “smart” devices following the concept of the Internet of Things and data-processing algorithms based on Machine Learning techniques allows developing promising frameworks to reach the best level of indoor thermal comfort closest to the real needs of users. The article investigates the potential of a new approach aiming at evaluating the effect of visual stimuli on personal thermal comfort perception through a comparison of 25 participants’ feedback exposed to a real scenario in a test cell and the same environment reproduced in Virtual Reality. The users’ biometric data and feedback about their thermal perception along with environmental parameters are collected in a dataset and managed with different Machine Learning techniques. The most suitable algorithm, among those selected, and the influential variables to predict the Personal Thermal Comfort Perception are identified. The Extra Trees classifier emerged as the most useful algorithm in this specific case. In real and virtual scenarios, the most important variables that allow predicting the target value are identified with an average accuracy higher than 0.99.

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Application of IoT and Machine Learning techniques for the assessment of thermal comfort perception.

Cited by 25 publications

References 15 publications

A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?

A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?

Indoor Air Quality Monitoring Systems Based on Internet of Things: A Systematic Review

Evaluation of the Visual Stimuli on Personal Thermal Comfort Perception in Real and Virtual Environments Using Machine Learning Approaches

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