Driving the Momentum Towards Adopting Wearable Cognitive Assistance in Lean Construction 4.0
Bibliographic record
Abstract
Despite the transformative potential of Wearable Cognitive Assistance Devices (WCADs), their integration into the construction industry remains limited, marked by challenges such as practicality and regulatory barriers.Additionally, the increasing interest in implementing Lean principles in construction for enhanced project performance creates a potential intersection.This study aims to bridge both concepts by developing a conceptual framework for the implementation of WCADs in construction tasks within the Lean Construction 4.0 paradigm.It first explores the current state of WCAD in various industries and proposes a WCAD implementation framework for construction.The framework employs a stepwise approach, and its theoretical implementation in masonry works illustrates its adaptability to specific construction contexts.This framework's contribution lies in its potential to offer dynamic, adaptive, and personalized support, optimizing cognitive functions, and promoting safer and more productive task execution.This framework utilizes wearable sensors as one it's data collection methods; thereafter, the integration of the data collected will then provide users with near real-time feedback to mitigate risks and enhance workers performance.As a theoretical foundation, this research paves the way for practical validation and future enhancements, aiming to enhance the construction industry's approach to worker well-being and performance.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".