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Record W3210728416

Rethinking Smart Home Design: Integrating Architectural Perspectives and Technologically-driven Design Thinking within a Framework

2021· dissertation· en· W3210728416 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueVTechWorks (Virginia Tech) · 2021
Typedissertation
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsnot available
FundersCanadian Institute for Theoretical Astrophysics
KeywordsArchitectural engineeringDesign thinkingArchitectural designEngineeringArchitectureSystems engineeringComputer scienceHuman–computer interactionVisual artsArt
DOInot available

Abstract

fetched live from OpenAlex

Smart homes, equipped with sensing, actuation, communication, and computation capabilities, enable automation and adaptation according to the occupants' needs. These capabilities work together to build holistic spatial and living experiences for the occupants. Smart technologies significantly impact spatial experiences, making smart home design an architectural problem along with a technological problem. Nevertheless, smart home research focuses primarily on standalone technological solutions, where the spatial/architectural aspect is largely absent. We argue that addressing the technological aspects isolated from the spatial context leads to reduced experiences for the users/occupants, as this practice blocks the pathways to develop holistic and innovative smart home solutions. Hence, we focus on bridging the gap between architectural and technological components in smart home research. To this end, we studied the design of smart homes from related disciplines, i.e., architecture, human-computer interaction, human--building interaction, industrial manufacturing, and modular assembly. Our research used the triangulation technique to consult with subject matter experts (researchers, practitioners, and professors of related disciplines) to understand current design processes. We conducted ethnographic studies, focus group studies, and in-depth interviews and identified challenges and best practices for smart home design process. Our investigation recognizes a nascent research problem where the technological and architectural aspects come together in the design thinking of smart home designers. We expanded the scope of design thinking to include three primary elements of smart homes- embedded technology, architectural elements, and occupants' needs. This multidisciplinary and complex process requires a well-defined design framework to methodically address all the issues associated with it. Hence, we developed a user-centered design framework, ArTSE, through an iterative Delphi study to guide the smart home design process. ArTSE stands for "Architecture and Technology in Smart Home DEsign". This framework guides user requirements collection using HCI models, technology decision making, interaction modalities selection, the decision support system for schematic design, technology infrastructure development, and production of the necessary documentation. This framework is an evolution of the normative theory in the architectural design process that caters to the needs of smart home design. For defining implementation strategies, we applied the framework to a case study-- a smart reconfigurable space design project. Overall, we document different aspects of the smart home design process and provide a comprehensive guideline for designers, researchers, and practitioners in this area.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.395
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.229
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it