The potential and challenges of inferring thermal comfort at home using commodity sensors

Huang, Chuan-Che; Yang, Rayoung; Newman, Mark

doi:10.1145/2750858.2805831

Cited by 37 publications

(43 citation statements)

References 22 publications

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“…This vision has been supported [9,12,38,40] and challenged [24,32,39] by HCI researchers and practitioners for decades. Second, the ongoing pursuit of the sustainable smart home [1,18,19,31,40]. In most smart home research these visions have been pursued separately and potentially undermine each other.…”

Section: Introductionmentioning

confidence: 99%

Exploring Hygge as a Desirable Design Vision for the Sustainable Smart Home

Jensen

Strengers

Raptis

et al. 2018

Proceedings of the 2018 Designing Interactive Systems Conference

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In this paper, we present an exploratory study of hygge as a low-energy design vision for the smart home. Hygge is a Danish concept that embodies aesthetic experiences related to conviviality, often shaped by orchestrating atmospheres through low-level lighting. To explore this vision, we probe two Australian households that already live with smart home lighting technology. We report on household reflections of embedding hygge into everyday life. We conclude by outlining future directions for exploring desirable and sustainable smart home visions.

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Section: Introductionmentioning

confidence: 99%

Exploring Hygge as a Desirable Design Vision for the Sustainable Smart Home

Jensen

Strengers

Raptis

et al. 2018

Proceedings of the 2018 Designing Interactive Systems Conference

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“…3). The reported results [27] of Ghahramani et al showed a precision of 93.3% and sensitivity of 56.22% without clarifying the precision and sensitivity of the uncomfortable states of warm and cool. On the other hand, our results of (Model III Conf.…”

Section: Discussionmentioning

confidence: 86%

“…In their study [27], Ghahramani et al used HMM classification technique, in which three classes of thermal comfort, namely, comfortable, uncomfortably cool and uncomfortably warm are used. An important point to be considered in the work of Ghahramani et al [27] is the class imbalance in their used experimental data between the positive class (comfortable), which represents 81% of the data and the negative class (uncomfortable), which represents only 19% of the data. Therefore, using the classification accuracy (reported 82.8 %) is considered misleading in this case.…”

Section: Discussionmentioning

confidence: 99%

Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach

et al. 2019

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Thermal comfort and sensation are important aspects of building design and indoor climate control, as modern man spends most of the day indoors. Conventional indoor climate design and control approaches are based on static thermal comfort/sensation models that view the building occupants as passive recipients of their thermal environment. To overcome the disadvantages of static models, adaptive thermal comfort models aim to provide opportunity for personalized climate control and thermal comfort enhancement. Recent advances in wearable technologies contributed to new possibilities in controlling and monitoring health conditions and human wellbeing in daily life. The generated streaming data generated from wearable sensors are providing a unique opportunity to develop a real-time monitor of an individual's thermal state. The main goal of this work is to introduce a personalized adaptive model to predict individual's thermal sensation based on non-intrusive and easily measured variables, which could be obtained from already available wearable sensors. In this paper, a personalized classification model for individual thermal sensation with a reduced-dimension input-space, including 12 features extracted from easily measured variables, which are obtained from wearable sensors, was developed using least-squares support vector machine algorithm. The developed classification model predicted the individual's thermal sensation with an overall average accuracy of 86%. Additionally, we introduced the main framework of streaming algorithm for personalized classification model to predict an individual's thermal sensation based on streaming data obtained from wearable sensors.The assessment of thermal sensation has been regarded as more reliable and as such is often used to estimate thermal comfort [4].Thermal sensation is the result of the body "psycho-physical reaction" to certain thermal stimuli related to indoor conditions [5]. Human thermal sensation mainly depends on the human body temperature (core body temperature), which is a function of sets of comfort factors [5,6]. These comfort factors include indoor environmental factors, such as mean air temperature around the body, relative air velocity around the body, humidity, and mean radiant temperature of the environment to the body [6]. Additionally, some personal (individual-related) factors, namely metabolic rate or internal heat production in the body, which vary with the activity level and clothing thermo-physical properties (such as clothing insulation and vapor clothing resistance), are included. It should be mentioned that the individual thermal perception is deepening, as well, on psychological factors, expectations and short/long-term experience, which directly affect individuals' perceptions, time of exposure, perceived control, and environmental stimulation [7]. The most considered way to have an accurate assessment of TS is to ask the individuals directly about their thermal sensation perception [5,6]. The thermal-sensation-vote (TSV) is one of the most used ...

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“…Existing HCI work on thermal comfort extends to wearable sensors for personal thermostat control [12] and for sensing thermal comfort [5,16]. Compact wrist-wearable pulse and skin temperature sensors can provide accurate real-time physiological thermal comfort information from multiple users [5,16].…”

Section: Background: Refining Smart Thermostat Operation Through Humamentioning

confidence: 99%

Log it While it's Hot

Snow

Auffenberg

schraefel

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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Smart thermostats offer impressive scope for adapting to users' thermal comfort preferences and saving energy in shared work environments. Yet human interactions with smart thermostats thus far amount to an assumption from designers that users are willing and able to provide unbiased data at regular intervals; which may be unrealistic. In this paper we highlight the variety of social factors which complicate users' relationships with smart thermostats in shared work environments. These include social dynamics, expectations, and contextually specific factors that influence motivations for interaction with the system. In response we outline our framework towards a Smarter Thermostat: one which better accounts for these messy social inevitabilities, is equipped for a decline in user feedback over time and one which augments rather than attempts to replaces human intelligence-thereby ensuring a smarter thermostat does not create dumber humans.

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The potential and challenges of inferring thermal comfort at home using commodity sensors

Cited by 37 publications

References 22 publications

Exploring Hygge as a Desirable Design Vision for the Sustainable Smart Home

Exploring Hygge as a Desirable Design Vision for the Sustainable Smart Home

Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach

Log it While it's Hot

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