Rethinking Smart Home Design: Integrating Architectural Perspectives and Technologically-driven Design Thinking within a Framework
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.049 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".