The Impact of Smart Interactive Technologies in Creating Personal Internal Spaces: An Analytical Study of User Preferences for Interactive Shape Characteristics
Bibliographic record
Abstract
As interactive technologies become increasingly prevalent in personal living spaces, understanding users' preferences and interactions with these technologies becomes crucial.This study aims to examine users' preferences for interactive technologies in personal living spaces, specifically focusing on interactive lighting, furniture, and space changes.The findings of this study will inform the design and development of future personalized interactive environments.A diverse group of participants completed a questionnaire assessing their preferences for interactive technologies in different home spaces.The collected data were analyzed using descriptive statistics.The results indicate a growing acceptance of interactive technologies in personal living spaces.Most respondents expressed a preference for interactive color change, followed by interactive furniture.Gender differences in preferences were also evident, with males showing a greater preference for form changes, while females favored interactive furniture.These findings have significant implications for the design of personalized interactive environments.It highlights the importance of considering users' preferences and involving them in the design process to create tailored experiences.This study contributes to the field by emphasizing the importance of researching interactive technologies and their potential applications in people's homes and environments.By providing valuable insights into designing and developing future personalized interactive environments, this research emphasizes the need to meet users' evolving needs and preferences to enhance their overall living experiences.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".