Thermal preferences and cognitive performance estimation via user's physiological responses

This study investigated the relationship between occupants' thermal sensation, physiological responses, and cognitive performance to quantify the priorities of the selected physiological responses for optimal productivity. In order to quantify variables for optimal productivity estimation, this study considered the following factors: 1. Local body skin temperature as an occupant's physiological responses; 2. Participants' individual factors such as gender; 3. Cognitive performance in operation span task; 4. Environmental data such as indoor temperature, wind velocity, CO2 level and indoor humidity; 5. Individual ratings of subjective thermal sensation. A series of human experiments were conducted to collect physiological responses and cognitive performance in a different room temperature conditions. The skin temperatures and environmental data were recorded in every minutes, and thermal sensation was surveyed by the Likert 7 point scale questionnaires. The operation span (OSPAN) task was used to measure working memory as a cognitive performance for occupant's productivity. Total 39 participants' data was collected for comparative analysis. The results revealed significant correlations between overall thermal sensation and local body skin temperatures. Also, the OSPAN score showed that it has a significant correlation with indoor temperature, thermal sensation as well as physiological responses. The OSPAN results were higher when indoor temperature was relatively low or when participant's thermal perception was either slightly cool or cool. Most local body skin temperatures were negatively correlated with the cognitive test scores, therefore it was concluded that a little low temperature has a significant impact to promote occupant's productivity. This study also determined the priority of local skin temperatures and gender by their impact to estimate the occupant's cognitive performance

Project Coolbit: Can your watch predict heat stress and thermal comfort sensation?

10.1088/1748-9326/abd130ENVIRONMENTAL RESEARCH LETTERS16

Design and evaluation of a body temperature controlled Air-conditioning system

While remote sensing technologies for airconditioners have been available for some time, no research has been done on airconditioner remote sensing of the body. This thesis looks at the opportunities for remotely sensing body temperature from the wrist. The goal of this report was to evaluate any potential energy savings to be had for airconditioners by utilising this measure of the body. A prototype was designed emphasising factors such as size, weight and energy consumption/battery life. The prototype was then evaluated for success by comparison with baseline energy use and the observance of a reduction in the coefficient of determination between outside air temperature and energy use. While dramatic energy savings were not realised due to the simplistic nature of the prototype, a saving of almost a kilowatt hour for sub 35ºC days was able to be achieved. These results show the promise that body temperature sensing offers

AutoTherm: A Dataset and Ablation Study for Thermal Comfort Prediction in Vehicles

State recognition in well-known and customizable environments such as vehicles enables novel insights into users and potentially their intentions. Besides safety-relevant insights into, for example, fatigue, user experience-related assessments become increasingly relevant. As thermal comfort is vital for overall comfort, we introduce a dataset for its prediction in vehicles incorporating 31 input signals and self-labeled user ratings based on a 7-point Likert scale (-3 to +3) by 21 subjects. An importance ranking of such signals indicates higher impact on prediction for signals like ambient temperature, ambient humidity, radiation temperature, and skin temperature. Leveraging modern machine learning architectures enables us to not only automatically recognize human thermal comfort state but also predict future states. We provide details on how we train a recurrent network-based classifier and, thus, perform an initial performance benchmark of our proposed thermal comfort dataset. Ultimately, we compare our collected dataset to publicly available datasets

Temperature and comfort monitoring systems for humans

PhDThermoregulation system and human body responses, both physiological (i.e. skin and core temperature) and psychological (thermal sensation and thermal comfort), have been of considerable interest to researchers. However, while reactions to extreme conditions are well understood and explained, there is a considerable knowledge gap for mild temperature range adaptation. Previous research focused on the whole body response, while local analysis is more appropriate for a new generation of intelligent thermal control systems such as needed in planes. Furthermore majority of previous studies were carried out predominantly on mannequins or with subjects placed in highly controlled lab chambers, hence adaptations in normal shared spaces is not investigated in sufficient depth. In addition, no study investigated infants’ temperature adaptation. This thesis describes the comprehensive study of the human temperature distribution in selected areas, both for adults and infants under the age of 2. Furthermore, variation of core and local skin temperature, thermal sensation and level of comfort due to long periods of inactivity were also investigated in adults. These studies have set the basis for the development of temperature monitoring systems. The first monitoring system specific to children under 2 provides fever detection based on skin temperature measurement. It was developed for a Spanish textile company (AITEX), and it is a patent under consideration. The second system monitors level of comfort and thermal sensation of adults in indoor environments. The system is based on pre-existing statistical studies and Fanger’s steady-state model. It adapts to the individual while analysing real time skin temperature distribution, and identifie

Sviluppo di un metodo innovativo per la misura del comfort termico attraverso il monitoraggio di parametri fisiologici e ambientali in ambienti indoor

openLa misura del comfort termico in ambienti indoor è un argomento di interesse per la comunità scientifica, poiché il comfort termico incide profondamente sul benessere degli utenti ed inoltre, per garantire condizioni di comfort ottimali, gli edifici devono affrontare costi energetici elevati. Anche se esistono norme nel campo dell'ergonomia del comfort che forniscono linee guida per la valutazione del comfort termico, può succedere che in contesti reali sia molto difficile ottenere una misurazione accurata. Pertanto, per migliorare la misura del comfort termico negli edifici, la ricerca si sta concentrando sulla valutazione dei parametri personali e fisiologici legati al comfort termico, per creare ambienti su misura per l’utente. Questa tesi presenta diversi contributi riguardo questo argomento. Infatti, in questo lavoro di ricerca, sono stati implementati una serie di studi per sviluppare e testare procedure di misurazione in grado di valutare quantitativamente il comfort termico umano, tramite parametri ambientali e fisiologici, per catturare le peculiarità che esistono tra i diversi utenti. In primo luogo, è stato condotto uno studio in una camera climatica controllata, con un set di sensori invasivi utilizzati per la misurazione dei parametri fisiologici. L'esito di questa ricerca è stato utile per ottenere una prima accuratezza nella misurazione del comfort termico dell'82%, ottenuta mediante algoritmi di machine learning (ML) che forniscono la sensazione termica (TSV) utilizzando la variabilità della frequenza cardiaca (HRV) , parametro che la letteratura ha spesso riportato legato sia al comfort termico dell'utenza che alle grandezze ambientali. Questa ricerca ha dato origine a uno studio successivo in cui la valutazione del comfort termico è stata effettuata utilizzando uno smartwatch minimamente invasivo per la raccolta dell’HRV. Questo secondo studio consisteva nel variare le condizioni ambientali di una stanza semi-controllata, mentre i partecipanti potevano svolgere attività di ufficio ma in modo limitato, ovvero evitando il più possibile i movimenti della mano su cui era indossato lo smartwatch. Con questa configurazione, è stato possibile stabilire che l'uso di algoritmi di intelligenza artificiale (AI) e il set di dati eterogeneo creato aggregando parametri ambientali e fisiologici, può fornire una misura di TSV con un errore medio assoluto (MAE) di 1.2 e un errore percentuale medio assoluto (MAPE) del 20%. Inoltre, tramite il Metodo Monte Carlo (MCM) è stato possibile calcolare l'impatto delle grandezze in ingresso sul calcolo del TSV. L'incertezza più alta è stata raggiunta a causa dell'incertezza nella misura della temperatura dell'aria (U = 14%) e dell'umidità relativa (U = 10,5%). L'ultimo contributo rilevante ottenuto con questa ricerca riguarda la misura del comfort termico in ambiente reale, semi controllato, in cui il partecipante non è stato costretto a limitare i propri movimenti. La temperatura della pelle è stata inclusa nel set-up sperimentale, per migliorare la misurazione del TSV. I risultati hanno mostrato che l'inclusione della temperatura della pelle per la creazione di modelli personalizzati, realizzati utilizzando i dati provenienti dal singolo partecipante, porta a risultati soddisfacenti (MAE = 0,001±0,0003 e MAPE = 0,02%±0,09%). L'approccio più generalizzato, invece, che consiste nell'addestrare gli algoritmi sull'intero gruppo di partecipanti tranne uno, e utilizzare quello tralasciato per il test, fornisce prestazioni leggermente inferiori (MAE = 1±0.2 e MAPE = 25% ±6%). Questo risultato evidenzia come in condizioni semi-controllate, la previsione di TSV utilizzando la temperatura della pelle e l'HRV possa essere eseguita con un certo grado di incertezza.Measuring human thermal comfort in indoor environments is a topic of interest in the scientific community, since thermal comfort deeply affects the well-being of occupants and furthermore, to guarantee optimal comfort conditions, buildings must face high energy costs. Even if there are standards in the field of the ergonomics of the thermal environment that provide guidelines for thermal comfort assessment, it can happen that in real-world settings it is very difficult to obtain an accurate measurement. Therefore, to improve the measurement of thermal comfort of occupants in buildings, research is focusing on the assessment of personal and physiological parameters related to thermal comfort, to create environments carefully tailored to the occupant that lives in it. This thesis presents several contributions to this topic. In fact, in the following research work, a set of studies were implemented to develop and test measurement procedures capable of quantitatively assessing human thermal comfort, by means of environmental and physiological parameters, to capture peculiarities among different occupants. Firstly, it was conducted a study in a controlled climatic chamber with an invasive set of sensors used for measuring physiological parameters. The outcome of this research was helpful to achieve a first accuracy in the measurement of thermal comfort of 82%, obtained by training machine learning (ML) algorithms that provide the thermal sensation vote (TSV) by means of environmental quantities and heart rate variability (HRV), a parameter that literature has often reported being related to both users' thermal comfort. This research gives rise to a subsequent study in which thermal comfort assessment was made by using a minimally invasive smartwatch for collecting HRV. This second study consisted in varying the environmental conditions of a semi-controlled test-room, while participants could carry out light-office activities but in a limited way, i.e. avoiding the movements of the hand on which the smartwatch was worn as much as possible. With this experimental setup, it was possible to establish that the use of artificial intelligence (AI) algorithms (such as random forest or convolutional neural networks) and the heterogeneous dataset created by aggregating environmental and physiological parameters, can provide a measure of TSV with a mean absolute error (MAE) of 1.2 and a mean absolute percentage error (MAPE) of 20%. In addition, by using of Monte Carlo Method (MCM), it was possible to compute the impact of the uncertainty of the input quantities on the computation of the TSV. The highest uncertainty was reached due to the air temperature uncertainty (U = 14%) and relative humidity (U = 10.5%). The last relevant contribution obtained with this research work concerns the measurement of thermal comfort in a real-life setting, semi-controlled environment, in which the participant was not forced to limit its movements. Skin temperature was included in the experimental set-up, to improve the measurement of TSV. The results showed that the inclusion of skin temperature for the creation of personalized models, made by using data coming from the single participant brings satisfactory results (MAE = 0.001±0.0003 and MAPE = 0.02%±0.09%). On the other hand, the more generalized approach, which consists in training the algorithms on the whole bunch of participants except one, and using the one left out for the test, provides slightly lower performances (MAE = 1±0.2 and MAPE = 25%±6%). This result highlights how in semi-controlled conditions, the prediction of TSV using skin temperature and HRV can be performed with acceptable accuracy.INGEGNERIA INDUSTRIALEembargoed_20220321Morresi, Nicol

Impact of an intermittent and localized cooling intervention on skin temperature, sleep quality and energy expenditure in free-living, young, healthy adults

Where people live and work together it is not always possible to modify the ambient temperature; ways must therefore be found that allow individuals to feel thermally comfortable in such settings. The Embr Wave (R) is a wrist-worn device marketed as a 'personal thermostat' that can apply a local cooling stimulus to the skin. The aim of the present study was to determine the effect of an intermittent mild cold stimulus of 25 degrees C for 15-20 s every 5 min over 3.5 days under free-living conditions on 1) skin temperature, 2) perception of skin temperature, 3) sleep quality and 4) resting energy expenditure (REE) in young, healthy adults. Ten subjects wore the device for 3.5 consecutive days. This intervention reduced distal skin temperature after correcting for personal ambient temperature (P = 0.051). Thus, this intermittent mild cold regime can reduce distal skin temperature, and wearing it under free-living conditions for 3.5 days does not seem to impair the perception of skin temperature and sleep quality or modify REE.The study was funded by the Spanish Ministry of Economy and Competitiveness via the Fondo de Investigacion Sanitaria del Instituto de Salud Carlos III (PI13/01393 and CB16/10/00239) and PTA 12264-I, Retos de la Sociedad (DEP2016-79512-R), and European Regional Development Funds (ERDF). Other funders included the Spanish Ministry of Education (FPU 16/05159, 15/04059 and 19/02326), the Fundacion Iberoamericana de Nutricion (FINUT), the Redes Tematicas De Investigacion Cooperativa RETIC (Red SAMID RD16/0022), the AstraZeneca Health Care Foundation, the University of Granada Plan Propio de Investigacion 2016 (Excellence actions: Unit of Excellence on Exercise, Nutrition and Health [UCEENS]), and by the Junta de Andalucia, Consejeria de Conocimiento, Investigacion y Universidades (ERDF, SOMM17/6107/UGR). AMT was supported by Seneca Foundation through grant 19899/GERM/15 and the Ministry of Science Innovation and Universities RTI2018-093528-B-I0, as well as DJP (MINECO; RYC-2014-16938). BMT was supported by an individual postdoctoral grant from the Fundacion Alfonso Martin Escudero. We thank Dr. Matt Smith of Embr Labs Inc. for configuring the Embr Wave (R) devices used in this experiment

Sensing Physiological Change and Mental Stress in Older Adults From Hot Weather

This study combines wearable sensors, weather data, and self-reported mood surveys to assess mental stress on older adults from heat experience. It is designed as a pilot and feasibility study in preparation for a large-scale experiment of older adults' mental wellbeing during extreme heat events. Results show that on-body temperatures from two i-Button sensors coupled with heart rate monitored from a smart watch are important indicators to evaluate individualized heat stress given a relatively uniform outdoor temperature. Furthermore, assessing their mood in their own environment demonstrates potential for understanding mental wellbeing that can change with varying time and location