Log it While it's Hot

Snow, Stephen; Auffenberg, Frederik; schraefel, m.c.

doi:10.1145/3025453.3025578

Cited by 13 publications

(7 citation statements)

References 25 publications

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“…Predicting user preferences and actions could for example be used to adjust temperature, heating or lighting conditions to suit their activities. Learning systems can gather sensor data through aforementioned sensors or they can learn inhabitants' preferences through userinput (Snow et al, 2017). The smart home can become activity aware when sensor data is labeled using an activity recognition algorithm (Dahmen et al, 2017).…”

Section: Ai: Learning Systemsmentioning

confidence: 99%

“…While the issues and limitations posed by these learning mechanisms often tend to be viewed as a challenge for data collection (Zuo and De With, 2005), the reality of an unpredictable "messy" everyday life begs for a more integrated approach. There are plenty of social factors that can complicate inhabitants' relationship with SHTs, including social dynamics, expectations, and contextually specific factors (Snow et al, 2017). The idea that SHTs can make informed and autonomic choices on, for example, energy consumption in the home conflicts with the reality of a domestic "mess" that implies a lack of order in an otherwise ordered reality (Strengers, 2014).…”

Section: Ai: Learning Systemsmentioning

confidence: 99%

“…In tackling these shortcomings, advances in ICT have made it possible to collect and communicate data in real time, as well as to implement data-driven pattern recognition and control algorithms (Jung and Jazizadeh, 2019). As a result, research has shifted to Human-In-The-Loop (HITL) operations, which rely on data from human experiences, such as accounting for occupant dynamics in indoor environments (e.g., occupancyrelated features such as presence, count, and position, and thermal comfort) (Snow et al, 2017;Jung and Jazizadeh, 2019). Through future developments, HVAC systems are expected to be aware of actual occupancy schedules, plan preferred conditions prior to occupant arrival, maintain them during occupancy, and change operations once the vacancy is verified to reduce energy waste (Jung and Jazizadeh, 2019).…”

Section: Heating and Cooling The Homementioning

confidence: 99%

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Learning Systems versus Future Everyday Domestic Life: A Designer’s Interpretation of Social Practice Imaginaries

Viaene

Kuijer

Funk

2021

Front. Artif. Intell.

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Smart home technologies with the ability to learn over time promise to adjust their actions to inhabitants’ unique preferences and circumstances. For example, by learning to anticipate their routines. However, these promises show frictions with the reality of everyday life, which is characterized by its complexity and unpredictability. These systems and their design can thus benefit from meaningful ways of eliciting reflections on potential challenges for integrating learning systems into everyday domestic contexts, both for the inhabitants of the home as for the technologies and their designers. For example, is there a risk that inhabitants’ everyday lives will reshape to accommodate the learning system’s preference for predictability and measurability? To this end, in this paper we build a designer’s interpretation on the Social Practice Imaginaries method as developed by Strengers et al. to create a set of diverse, plausible imaginaries for the year 2030. As a basis for these imaginaries, we have selected three social practices in a domestic context: waking up, doing groceries, and heating/cooling the home. For each practice, we create one imaginary in which the inhabitants’ routine is flawlessly supported by the learning system and one that features everyday crises of that routine. The resulting social practice imaginaries are then viewed through the perspective of the inhabitant, the learning system, and the designer. In doing so, we aim to enable designers and design researchers to uncover a diverse and dynamic set of implications the integration of these systems in everyday life pose.

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Section: Ai: Learning Systemsmentioning

confidence: 99%

Section: Ai: Learning Systemsmentioning

confidence: 99%

Section: Heating and Cooling The Homementioning

confidence: 99%

See 1 more Smart Citation

Learning Systems versus Future Everyday Domestic Life: A Designer’s Interpretation of Social Practice Imaginaries

Viaene

Kuijer

Funk

2021

Front. Artif. Intell.

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“…It might have made it more convenient for some of the occupants to cast a vote when their motivation for voting was less prominent than when they were uncomfortable. The aforementioned factors might also explain why some studies on OVS (Sanguinetti et al, 2017;Snow et al, 2017;Lassen et al, 2020) reported that comfortable occupants tended to vote less frequently.…”

Section: Impact Of Location and Design On Occupants' Interaction Withmentioning

confidence: 99%

Occupants’ Interaction With an Occupant Voting System for Thermal and Indoor Air Quality Feedback – Case Studies in Office Spaces

Khan

Kolařík

Weitzmann

2021

Front. Built Environ.

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Occupants can provide valuable feedback on the indoor environmental quality of buildings. Research on occupant voting systems (OVS) has demonstrated that apps or electronic devices can act as viable tools for collecting long-term feedback from occupants. However, previous research has only to a limited extent explored occupants’ interaction with OVS and the impact it might have on the reliability of the collected feedback. The present paper presents three case studies on applying a tangible OVS, denoted TiAQ, to collect feedback on thermal and indoor air quality (IAQ) in office spaces. The main objective of the present study was first to explore occupants’ interaction with TiAQ and their motivation. Second, identifying whether feedback collected with TiAQ could represent occupants’ comfort and discomfort with the thermal environment and IAQ. Thirdly, identifying challenges related to occupants’ interaction with TiAQ that might affect the representativeness of collected feedback. The present study was conducted at three office buildings in up to 7 months and demonstrated that TiAQ was suitable for collecting long-term feedback on occupants’ comfort and discomfort with the thermal environment. On average, one vote was cast per day per occupant. Additionally, in one of the office buildings, 60% of occupants reported to have interacted with TiAQ daily or weekly when, e.g., passing the device going to/from the office spaces. Occupants’ expectations of TiAQ and getting “feedback on feedback” need to be further addressed to identify ways to sustain occupants’ long-term use of the device and reduce “ill-willed” interactions.

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“…A breadth of research has established relationships between physical and environmental factors, and the wellbeing and productivity of employees in office spaces using sensory devices (Alavi et al, 2017;Snow et al, 2017;Aryal et al, 2018;Clear et al, 2018;Constantinides et al, 2020;Lee et al, 2021). Some works study the impact of static spatial features on social interactions and social behavior in the workplace (Mark et al, 2014;Mark et al, 2015;Alavi et al, 2018b).…”

Section: Introducing Ambient Workpacesmentioning

confidence: 99%

Understanding the Experiences of Remote Workers: Opportunities for Ambient Workspaces at Home

Margariti

Ali

Grave

et al. 2021

Front. Comput. Sci.

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As the COVID-19 pandemic has forced many to work remotely from home, collaborating solely through digital technologies, a growing population of remote home workers are faced with profound wellbeing challenges. Passive sensing devices and ambient feedback have great potential to support the wellbeing of the remote workers, but there is a lack of background and understanding of the domestic workplace in terms of physical and affective dimensions and challenges to wellbeing. There are profound research gaps on wellbeing in the domestic workplace, with the current push for remote home and hybrid working making this topic timely. To address these gaps and shape a starting point for an “ambient workspaces” agenda, we conducted an exploratory study to map physical and affective aspects of working from home. The study involved both qualitative and quantitative measures of occupant experience, including sensor wristbands, and a custom web application for self-reporting mood and aspects of the environment. It included 13 participants for a period of 4 weeks, during a period of exclusive home working. Based on quantitative and qualitative analysis, our study addresses wellbeing challenges of the domestic workplace, establishes correlations between mood and physical aspects, and discusses the impact of feedback mechanisms in the domestic workplace on the behavior of remote workers. Insights from these observations are then used to inform a future design agenda for ambient technologies that supports the wellbeing of remote workers; addressing the design opportunities for ambient interventions in domestic workspaces. This work offers three contributions: 1) qualitatively and quantitatively informed understandings of the experiences of home-workers; 2) a future design agenda for “ambient home workspaces”; and 3) we propose three design concepts for ambient feedback and human–AI interactions in the built environment, to illustrate the utility of the design agenda.

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Log it While it's Hot

Cited by 13 publications

References 25 publications

Learning Systems versus Future Everyday Domestic Life: A Designer’s Interpretation of Social Practice Imaginaries

Learning Systems versus Future Everyday Domestic Life: A Designer’s Interpretation of Social Practice Imaginaries

Occupants’ Interaction With an Occupant Voting System for Thermal and Indoor Air Quality Feedback – Case Studies in Office Spaces

Understanding the Experiences of Remote Workers: Opportunities for Ambient Workspaces at Home

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