Wearable devices and Machine Learning algorithms to assess indoor thermal sensation: metrological analysis
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[1] S. Schiavon,et al. Personal comfort models based on a 6‐month experiment using environmental parameters and data from wearables , 2022, Indoor air.
[2] I. Pigliautile,et al. A novel methodology for human thermal comfort decoding via physiological signals measurement and analysis , 2022, Building and Environment.
[3] G. Cosoli,et al. Combined use of wearable devices and Machine Learning for the measurement of thermal sensation in indoor environments , 2022, 2022 IEEE International Workshop on Metrology for Living Environment (MetroLivEn).
[4] G. Cosoli,et al. The importance of physiological data variability in wearable devices for digital health applications , 2022, ACTA IMEKO.
[5] Bluyssen,et al. Routledge Handbook of Resilient Thermal Comfort , 2022 .
[6] I. Pigliautile,et al. The NEXT.ROOM: Design principles and systems trials of a novel test room aimed at deepening our knowledge on human comfort , 2022, Building and Environment.
[7] G. M. Revel,et al. Impact of the measurement uncertainty on the monitoring of thermal comfort through AI predictive algorithms , 2021, ACTA IMEKO.
[8] Xianguo Wu,et al. Optimizing energy efficiency and thermal comfort in building green retrofit , 2021 .
[9] Tibor Bosse,et al. Detecting Emotions through Electrodermal Activity in Learning Contexts: A Systematic Review , 2021, Sensors.
[10] Lorenzo Scalise,et al. Measurement of multimodal physiological signals for stimulation detection by wearable devices , 2021 .
[11] C. Chun,et al. Differences between EEG during thermal discomfort and thermal displeasure , 2021 .
[12] P. Wargocki,et al. Investigating the relation between electroencephalogram, thermal comfort, and cognitive performance in neutral to hot indoor environment. , 2021, Indoor air.
[13] Hansaem Park,et al. Prediction of individual thermal comfort based on ensemble transfer learning method using wearable and environmental sensors , 2021, Building and Environment.
[14] I. Pigliautile,et al. Measuring human physiological indices for thermal comfort assessment through wearable devices: A review , 2021 .
[15] Andres J. Rodriguez,et al. Sources of Inaccuracy in Photoplethysmography for Continuous Cardiovascular Monitoring , 2021, Biosensors.
[16] M. Krashes,et al. Preoptic BRS3 neurons increase body temperature and heart rate via multiple pathways , 2021, bioRxiv.
[17] I A Mayatskaya,et al. Eco-sustainable architecture and comfortable living environment , 2021 .
[18] S. M. Williams,et al. Stages of Sleep , 2020, Sleep Insights.
[19] C. Héberlé,et al. Normal EEG during the neonatal period: maturational aspects from premature to full-term newborns , 2020, Neurophysiologie Clinique.
[20] Nir Milstein,et al. Validating Measures of Electrodermal Activity and Heart Rate Variability Derived From the Empatica E4 Utilized in Research Settings That Involve Interactive Dyadic States , 2020, Frontiers in Behavioral Neuroscience.
[21] Ming Jin,et al. Personal thermal comfort models with wearable sensors , 2019, Building and Environment.
[22] P. Wargocki,et al. Changes in EEG signals during the cognitive activity at varying air temperature and relative humidity , 2019, Journal of Exposure Science & Environmental Epidemiology.
[23] Riccardo Bernardini,et al. Driver’s stress detection using Skin Potential Response signals , 2018, Measurement.
[24] Lihua Xie,et al. Random forest based thermal comfort prediction from gender-specific physiological parameters using wearable sensing technology , 2018 .
[25] S. Schiavon,et al. Thermal comfort and self-reported productivity in an office with ceiling fans in the tropics , 2018 .
[26] Leen Lauriks,et al. A review of human thermal comfort experiments in controlled and semi-controlled environments , 2018 .
[27] Joyce Kim,et al. Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning , 2018 .
[28] Young-Ho Cho,et al. Wearable Sweat Rate Sensors for Human Thermal Comfort Monitoring , 2018, Scientific Reports.
[29] Chad C. Williams,et al. Choosing MUSE: Validation of a Low-Cost, Portable EEG System for ERP Research , 2017, Front. Neurosci..
[30] Tongning Wu,et al. Effects of stimulus mode and ambient temperature on cerebral responses to local thermal stimulation: An EEG study. , 2017, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[31] Luca Citi,et al. cvxEDA: A Convex Optimization Approach to Electrodermal Activity Processing , 2016, IEEE Transactions on Biomedical Engineering.
[32] Diane J. Cook,et al. Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data , 2015 .
[33] E. Ostertagová,et al. Methodology and Application of the Kruskal-Wallis Test , 2014 .
[34] Domen Novak,et al. Metrological evaluation of skin conductance measurements , 2013 .
[35] M. Brzeziński. The Chen–Shapiro Test for Normality , 2012 .
[36] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[38] Sylvia D. Kreibig,et al. Autonomic nervous system activity in emotion: A review , 2010, Biological Psychology.
[39] L. Breslow,et al. A quantitative approach to the World Health Organization definition of health: physical, mental and social well-being. , 1972, International journal of epidemiology.
[40] Yan Li,et al. Rules-Based and SVM-Q Methods With Multitapers and Convolution for Sleep EEG Stages Classification , 2022, IEEE Access.
[41] G. M. Revel,et al. MEASURING METABOLIC RATE TO IMPROVE COMFORT MANAGEMENT IN BUILDINGS , 2018 .
[42] Uwe Herwig,et al. Using the International 10-20 EEG System for Positioning of Transcranial Magnetic Stimulation , 2004, Brain Topography.
[43] L. Hejjel,et al. Heart rate variability analysis. , 2001, Acta physiologica Hungarica.
[44] Standard Ashrae. Thermal Environmental Conditions for Human Occupancy , 1992 .