Longitudinal personal thermal comfort preference data in the wild

Quintana, Matias; Abdelrahman, Mahmoud; Frei, Mario; Tartarini, Federico; Miller, Clayton

doi:10.1145/3485730.3493693

Cited by 8 publications

(8 citation statements)

References 8 publications

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“…A summary of the methodology workflow is shown in Figure 5. In this diagram, Component 1 and 2 represent the two sets of thermal comfort data collection experiments that are used for the analysis [46,25]. The data sets for these two studies can be found in open-access Github repositories 1 , 2 .…”

Section: Methodsmentioning

confidence: 99%

Targeting occupant feedback using digital twins: Adaptive spatial-temporal thermal preference sampling to optimize personal comfort models

Abdelrahman,

Miller

2022

Preprint

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Collecting intensive longitudinal thermal preference data from building occupants is emerging as an innovative means of characterizing the performance of buildings and the people who use them. These techniques have occupants giving subjective feedback using smartphones or smartwatches frequently over the course of days or weeks. The intention is that the data will be collected with high spatial and temporal diversity to best characterize a building and the occupant's preferences. But in reality, leaving the occupant to respond in an ad-hoc or fixed interval way creates unneeded survey fatigue and redundant data. This paper outlines a scenario-based (virtual experiment) method for optimizing data sampling using a smartwatch to achieve comparable accuracy in a personal thermal preference model with less data. This method uses BIM-extracted spatial data, and Graph Neural Network (GNN) based modeling to find regions of similar comfort preference to identify the best scenarios for triggering the occupant to give feedback. This method is compared to two baseline scenarios based on the spatial context of specific spaces and 4 x 4 m grid squares in the building using a theoretical implementation on two field-collected data sets. The results show that the proposed Build2Vec method is 18-23% more in the overall sampling quality than the spaces-based and the square-grid-based sampling methods. The Build2Vec method also has similar performance to the baselines when removing redundant occupant feedback points but with better scalability potential.

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Section: Methodsmentioning

confidence: 99%

Targeting occupant feedback using digital twins: Adaptive spatial-temporal thermal preference sampling to optimize personal comfort models

Abdelrahman,

Miller

2022

Preprint

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“…Accordingly, the integration of the momentary assessment with IoT techniques could revolutionize how the data in the built environment is tagged with information generated by occupants. Smartphone and smartwatch apps can be leveraged to create ecological momentary assessments that prompt a user to provide instantaneous feedback in an elegant and least-intrusive way (Moskowitz and Young, 2006;Jayathissa et al, 2020;Sae-Zhang et al, 2020;Quintana et al, 2021;Abdelrahman et al, 2022). An example of wearable devices that can be leveraged to create ecological momentary assessments that prompt a user to Frontiers in Built Environment frontiersin.org provide instantaneous feedback in an elegant way (Moskowitz and Young, 2006;Sae-Zhang et al, 2020;Quintana et al, 2021;Abdelrahman et al, 2022).…”

Section: Data Visualization and Human-in-theloop Interactive Technolo...mentioning

confidence: 99%

“…Smartphone and smartwatch apps can be leveraged to create ecological momentary assessments that prompt a user to provide instantaneous feedback in an elegant and least-intrusive way (Moskowitz and Young, 2006;Jayathissa et al, 2020;Sae-Zhang et al, 2020;Quintana et al, 2021;Abdelrahman et al, 2022). An example of wearable devices that can be leveraged to create ecological momentary assessments that prompt a user to Frontiers in Built Environment frontiersin.org provide instantaneous feedback in an elegant way (Moskowitz and Young, 2006;Sae-Zhang et al, 2020;Quintana et al, 2021;Abdelrahman et al, 2022). An example of using smartphone apps is discussed by Liu et al (2018) where subjects are prompted by their cell phone (through a text reminder) to take an online survey and report their "right now" thermal comfort as often as possible.…”

Section: Data Visualization and Human-in-theloop Interactive Technolo...mentioning

confidence: 99%

Transformational IoT sensing for air pollution and thermal exposures

Pantelic

Nazarian

Miller

et al. 2022

Front. Built Environ.

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Cities today encounter significant challenges pertaining to urbanization and population growth, resource availability, and climate change. Concurrently, unparalleled datasets are generated through Internet of Things (IoT) sensing implemented at urban, building, and personal scales that serve as a potential tool for understanding and overcoming these issues. Focusing on air pollution and thermal exposure challenges in cities, we reviewed and summarized the literature on IoT environmental sensing on urban, building, and human scales, presenting the first integrated assessment of IoT solutions from the data convergence perspective on all three scales. We identified that there is a lack of guidance on what to measure, where to measure, how frequently to measure, and standards for the acceptable measurement quality on all scales of application. The current literature review identified a significant disconnect between applications on each scale. Currently, the research primarily considers urban, building, and personal scale in isolation, leading to significant data underutilization. We addressed the scientific and technological challenges and opportunities related to data convergence across scales and detailed future directions of IoT sensing along with short- and long-term research and engineering needs. IoT application on a personal scale and integration of information on all scales opens up the possibility of developing personal thermal comfort and exposure models. The development of personal models is a vital promising area that offers significant advancements in understanding the relationship between environment and people that requires significant further research.

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“…Works that are focused on constructing personalized comfort models typically use not only environmental sensors and a method to solicit perceived comfort feedback, but also comfort-related physiological measurements, such as skin temperature, heart rate, and acceleration [ 12 , 13 , 14 , 15 ]. Nkurikiyeyezu et al [ 12 ] used a heart rate monitor to collect physiological data along with comfort surveys to construct a comfort model.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

“…Similarly, Liu et al [ 14 ] used wearable skin temperature sensors (such as the iButton) and accelerometers as physiological sensors. Lastly, Quintana et al [ 15 ] used a smartwatch—which provides heart rate and resting heart rate—along with wearable temperature sensors attached to the smartwatch to collect data from several occupants in three buildings.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

An Open-Source Platform for Indoor Environment Monitoring with Participatory Comfort Sensing

Rosenberger

Guo

Coffman

et al. 2022

Sensors

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We present an open-source wireless network and data management system for collecting and storing indoor environmental measurements and perceived comfort via participatory sensing in commercial buildings. The system, called a personal comfort and indoor environment measurement (PCIEM) platform, consists of several devices placed in office occupants’ work areas, a wireless network, and a remote database to store the data. Each device, called a PCFN (personal comfort feedback node), contains a touchscreen through which the occupant can provide feedback on their perceived comfort on-demand, and several sensors to collect environmental data. The platform is designed to be part of an indoor climate control system that can enable personalized comfort control in real-time. We describe the design, prototyping, and initial deployment of a small number of PCFNs in a commercial building. We also provide lessons learned from these steps. Application of the data collected from the PCFNs for modeling and real-time control will be reported in future work. We use hardware components that are commercial and off-the-shelf, and our software design is based on open-source tools that are freely and publicly available to enable repeatability.

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Longitudinal personal thermal comfort preference data in the wild

Cited by 8 publications

References 8 publications

Targeting occupant feedback using digital twins: Adaptive spatial-temporal thermal preference sampling to optimize personal comfort models

Targeting occupant feedback using digital twins: Adaptive spatial-temporal thermal preference sampling to optimize personal comfort models

Transformational IoT sensing for air pollution and thermal exposures

An Open-Source Platform for Indoor Environment Monitoring with Participatory Comfort Sensing

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