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

Abstract

Global climate is changing as a result of anthropogenic warming, leading to higher daily excursions of temperature in cities. Such elevated temperatures have great implications on human thermal comfort and heat stress, which should be closely monitored. Current methods for heat exposure assessments (surveys, microclimate measurements, and laboratory experiments), however, present several limitations: measurements are scattered in time and space and data gathered on outdoor thermal stress and comfort often does not include physiological and behavioral parameters. To address these shortcomings, Project Coolbit aims to introduce a human-centric approach to thermal comfort assessments. In this study, we propose and evaluate the use of wrist-mounted wearable devices to monitor environmental and physiological responses that span a wide range of spatial and temporal distributions. We introduce an integrated wearable weather station that records (a) microclimate parameters (such as air temperature and humidity), (b) physiological parameters (heart rate, skin temperature and humidity), and (c) subjective feedback. The feasibility of this methodology to assess thermal comfort and heat stress is then evaluated using two sets of experiments: controlled-environment physiological data collection, and outdoor environmental data collection. We find that using the data obtained through the wrist-mounted wearables, core temperature can be predicted non-invasively with 95 percent of target attainment within ±0.27 °C. Additionally, a direct connection between the air temperature at the wrist (T a,w ) and the perceived activity level (PAV) of individuals was drawn. We observe that with increased T a,w , the desire for physical activity is significantly reduced, reaching ‘Transition only’ PAV level at 36 °C. These assessments reveal that the wearable methodology provides a comprehensive and accurate representation of human heat exposure, which can be extended in real-time to cover a large spatial distribution in a given city and quantify the impact of heat exposure on human life.

The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future

“…Physical exercise provides mental health benefits and reduces the risk of cardiovascular disease, diabetes, cancer, cognitive decline, and all-cause mortality. [69][70][71][72][73] However, high temperatures can reduce the frequency of physical activity, duration of physical activity, and the desire to engage in exercise, [74][75][76] and even low amounts of physical activity in high temperatures can pose a risk to health. 77 This indicator estimates the loss of potential hours of safe physical activity per person due to ambient temperature, humidity, and radiant heat, by tracking the hours per day that the wet bulb globe temperature exceeds 28°C, a threshold above which the national sports medicine authorities of the USA, Australia, and Japan recommend outdoor physical activities are done with discretion.…”

CrowdQC+—A Quality-Control for Crowdsourced Air-Temperature Observations Enabling World-Wide Urban Climate Applications

“…In recent years, the collection and utilisation of vast amounts of data via crowdsourcing, i.e., the collection of data from nontraditional sources via the internet (Muller et al, 2015), has gained much attention. Such non-traditional, opportunistic sources of data are, e.g., smartphones (e.g., Overeem et al, 2013b;Mass and Madaus 2014;Droste et al, 2017), smart wearable devices (Nazarian et al, 2021), cars (e.g., Haberlandt and Sester 2010;Bartos et al, 2019), commercial microwave links (e.g., Messer et al, 2006;Overeem et al, 2013a;Chwala and Kunstmann 2019), and privately-owned weather stations, called citizen weather stations (CWS) in the following (e.g., Steeneveld et al, 2011;Wolters and Brandsma 2012;Bell et al, 2013;Madaus et al, 2014;Chapman et al, 2017;de Vos et al, 2017;Venter et al, 2021). Each type of these data sources alone or multiple combined can be used in different meteorological and climatological applications, such as weather forecast (e.g., Mass and Madaus, 2014;Nipen et al, 2020), operational weather monitoring (e.g., de Vos et al, 2019), mesoscale model evaluation (e.g., Hammerberg et al, 2018), hydrometeorological analyses and modelling (e.g., Smiatek et al, 2017;de Vos et al, 2020), high-resolution mapping of air temperature (e.g., Venter et al, 2020;Vulova et al, 2020;Zumwald et al, 2021), thermal-comfort assessment (Nazarian et al, 2021), and urban climate investigations (e.g., Fenner et al, 2017Fenner et al, , 2019Droste et al, 2020;Feichtinger et al, 2020).…”

Quantifying Local and Mesoscale Drivers of the Urban Heat Island of Moscow with Reference and Crowdsourced Observations

“…In recent years, the use of nontraditional and opportunisticsensing technologies in meteorological and climatological research, such as smartphones (Overeem et al, 2013b;Mass and Madaus, 2014;Droste et al, 2017), cars (Haberlandt and Sester, 2010;Mahoney and O'Sullivan, 2013;Bartos et al, 2019), commercial microwave links (Messer et al, 2006;Zinevich et al, 2009;Overeem et al, 2013a;Chwala and Kunstmann, 2019), wrist-mounted wearables (Nazarian et al, 2020), and privately owned citizen weather stations (CWSs), e.g., Wolters and Brandsma (2012), Bell et al (2015), de Vos et al (2017, Meier et al (2017), Fenner et al (2019, Droste et al (2020), and Mandement and Caumont (2020), have shown to provide additional and reliable information, thus, highlighting a multitude of possible applications in research and beyond (de Vos et al, 2019;Nipen et al, 2020). To study urban air temperatures and the UHI effect, data from CWSs have been used in a variety of studies (Steeneveld et al, 2011;Chapman et al, 2017;Fenner et al, 2017;de Vos et al, 2020;Feichtinger et al, 2020;Venter et al, 2020;Vulova et al, 2020), focusing on different cities.…”