Using Serious Games in Virtual Reality for Automated Close Call and Contact Collision Analysis in Construction Safety
Bibliographic record
Abstract
Using Serious Games in Virtual Reality for Automated Close Call and Contact Collision Analysis in Construction Safety Olga Golovina, Caner Kazanci, Jochen Teizer and Markus König Pages 967-974 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Injuries and fatalities resulting from workplace accidents remain a global concern within the construction industry. While education and training of personnel offer well known approaches for establishing a safe work environment, Serious Games in Virtual Reality (VR) is being increasingly investigated as a complementary approach for learning. They yet have to take full advantage of the inherent data that can be collected about players. This research presents a novel approach for the automated assessment of players data. The proposed method gathers and processes the data within a serious game for instant personalized feedback. The application focuses on close calls and contact collisions between construction workers and hazards like equipment, harmful substances, or restricted work zones. The results demonstrate the benefits and limitations of safety information previously unavailable, or very hard or impossible to collect. An outlook presents work ahead for practical implementation in existing risk management processes. Keywords: accident investigation; close call; construction safety; equipment contact collisions; hazard; human-hazard interaction; risk prevention; serious game; situational awareness; virtual reality; workforce education; training DOI: https://doi.org/10.22260/ISARC2019/0129 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".