A novel methodology for human thermal comfort decoding via physiological signals measurement and analysis

Mansi, Silvia Angela; Pigliautile, Ilaria; Arnesano, Marco; Pisello, Anna Laura

doi:10.1016/j.buildenv.2022.109385

Cited by 40 publications

(8 citation statements)

References 49 publications

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“…Indeed, the SNS controls this phenomenon, and its activation is more marked in warm condition, when heat dissipation mechanisms are actuated [48]. Concerning EEG (d), it is possible to observe that the PSD values decrease in the case of hot TS; in particular, when the subject is in a cold environment, her/his attention level tends to be higher (corresponding to an intense mental activity) and this is reflected at least in Beta and Gamma waves (frequency > 35 Hz) [49].…”

Section: Resultsmentioning

confidence: 98%

“…The experimental campaign data could be also further analysed (and also widened, in terms of both numerosity and physiological variability) in order to develop Personalized Comfort Models, considering not only hot and warm thermal sensation, but also the neutral condition. Indeed, some of the considered features (e.g., from EDA and SKT signals) have been already proved to be suitable for the identification of neutral TS [49]. These models could cover pivotal roles in control and actuation perspectives within living environments, focusing on the real users' needs.…”

Section: Discussionmentioning

confidence: 99%

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Wearable devices and Machine Learning algorithms to assess indoor thermal sensation: metrological analysis

Cosoli,

Mansi,

Revel

et al. 2023

Acta IMEKO

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Personal comfort modeling is considered the most promising solution for indoor thermal comfort management in buildings. The use of wearable sensors is investigated for the real-time measurement of physiological signals to train comfort models for buildings monitoring and control. To achieve the required reliability, different uncertainty sources should be considered and weighted in the measurement results evaluation. This study presents an example of personal comfort model (PCM) development based on wearable sensors (i.e., Empatica E4 smartband and MUSE headband) acquiring multimodal signals (i.e., photoplethysmographic – PPG, electrodermal activity – EDA, skin temperature – SKT, and electroencephalographic – EEG ones), together with a metrological characterization of the modeling procedure. Starting from the data collected within an experimental campaign on 76 subjects, different Machine Learning (ML) algorithms were exploited to create comfort models capable of predicting the human thermal sensation (TS). The most accurate model was considered to investigate the impact of sensors uncertainty through a Monte Carlo simulation. Results showed that the Random Forest model is the best performing one (accuracy: 0.86). Monte Carlo simulation method proved that the model is very robust towards measurement uncertainties of input features (expanded uncertainty of the model accuracy: ± 0.04, k = 2). This confirms the possibility to derive the subject’s TS exploiting only physiological signals; measurement uncertainty is influenced mostly by PPG and EDA signals. This kind of investigation could lead to the development of PCMs, exploitable within control systems to optimize subjects’ well-being and building energy efficiency.

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Section: Resultsmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Wearable devices and Machine Learning algorithms to assess indoor thermal sensation: metrological analysis

Cosoli,

Mansi,

Revel

et al. 2023

Acta IMEKO

View full text Add to dashboard Cite

show abstract

“…The headband's built-in sensor enables it possible to observe movement during meditation. It encourages self-awareness and ideal alignment [18]. An intuitive user interface is provided by the Muse 2 headband's companion app, which connects to a smartphone or tablet via Bluetooth [19].…”

Section: Muse 2 Headbandmentioning

confidence: 99%

Enhancing Meditation Techniques and Insights Using Feature Analysis of Electroencephalography (EEG)

Khadam,

Abdulhameed,

Hammad

2024

Al-Mustansiriyah Journal of Science

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Through a Bluetooth connection between the Muse 2 device and the meditation app, leveraging IoT capabilities. The methodology encompasses data collection, preprocessing, feature extraction, and model training, all while utilizing Internet of Things (IoT) functionalities. The Muse 2 device records EEG data from multiple electrodes, which is then processed and analyzed within a mobile meditation platform. Preprocessing steps involve eliminating redundant columns, handling missing data, normalizing, and filtering, making use of IoT-enabled techniques. Feature extraction is carried out on EEG signals, utilizing statistical measures such as mean, standard deviation, and entropy. Three different models, including Support Vector Machine (SVM), Random Forest, and Multi-Layer Perceptron (MLP), are trained using the preprocessed data, incorporating Internet of Things (IoT) based methodologies. Model performance is assessed using metrics like accuracy, precision, recall, and F1-score, highlighting the effectiveness of IoT-driven techniques. Notably, the MLP and Random Forest models demonstrate remarkable accuracy and precision, underlining the potential of this IoT-integrated approach. Specifically, the three models achieved high accuracies, with Random Forest leading at 0.999, followed by SVM at 0.959 and MLP at 0.99. This study not only contributes to the field of brain-computer interfaces and assistive technologies but also showcases a viable method to seamlessly integrate the Muse 2 device into meditation practices, promoting self-awareness and mindfulness with the added power of IoT technology.

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“…Research findings indicate that, apart from behavioral factors, the participants' thermal experiences in a virtual reality heat environment they created were significantly higher than in the real environment, but not enough to affect the experimental results [41,42]. Additionally, participants' physiological signals such as electroencephalogram (EEG), heart rate variability (HRV), electrodermal activity (EDA), and skin temperature (ST) uniquely changed when the thermal comfort level changed in the virtual environment [43]. The heart rate exhibited a negative correlation with thermal comfort, with HRV approaching 1 under comfortable thermal conditions, and higher HRV values observed when participants felt thermal discomfort [43,44].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, participants' physiological signals such as electroencephalogram (EEG), heart rate variability (HRV), electrodermal activity (EDA), and skin temperature (ST) uniquely changed when the thermal comfort level changed in the virtual environment [43]. The heart rate exhibited a negative correlation with thermal comfort, with HRV approaching 1 under comfortable thermal conditions, and higher HRV values observed when participants felt thermal discomfort [43,44].…”

Section: Introductionmentioning

confidence: 99%

Virtual Reality-Based Digital Landscape Experience and Climate Change Monitoring: Evidence from Human Thermal Comfort

Lin,

Wang,

Yuan

et al. 2024

Sustainability

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With the frequent occurrence of extreme weather in various parts of the world, people have begun to reflect on the scientific rationality of the means of global climate change governance. How to effectively respond to the hazards caused by extreme weather remains a hot issue of concern to the international community. In this paper, taking the function of plant carbon sequestration and oxygen release, which can regulate human thermal comfort as an entry point, we use virtual reality (VR) to construct a digital scene and invite subjects to conduct behavioral experiments in order to assess human thermal comfort. The experimental results indicate that participants’ subjective evaluations of virtual and real environments are consistent with changes in heart rate variability (HRV), validating the reliability of using virtual environments to study thermal perception. The study also found a significant correlation between HRV and the wet bulb globe temperature (WBGT) index, which both measure human thermal comfort. This suggests that when the WBGT changes due to microclimate variations, HRV changes accordingly. The negative correlation between plant carbon sequestration oxygen release capacity and HRV further supports this view. It also indicates that human thermal comfort can provide feedback on microclimate change trends, and that accurate monitoring of the microclimate is more conducive to assessing the progress of climate warming. This study demonstrates the association between human comfort and microclimate change, discusses the validity of human thermal comfort metrics in climate monitoring, and provides new perspectives for solving the global climate crisis.

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A novel methodology for human thermal comfort decoding via physiological signals measurement and analysis

Cited by 40 publications

References 49 publications

Wearable devices and Machine Learning algorithms to assess indoor thermal sensation: metrological analysis

Wearable devices and Machine Learning algorithms to assess indoor thermal sensation: metrological analysis

Enhancing Meditation Techniques and Insights Using Feature Analysis of Electroencephalography (EEG)

Virtual Reality-Based Digital Landscape Experience and Climate Change Monitoring: Evidence from Human Thermal Comfort

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