Detecting the Hazards of Lifting and Carrying in Construction through a Coupled 3D Sensing and IMUs Sensing System
Bibliographic record
Abstract
Construction companies in Hong Kong suffer huge losses due to labor fatalities and injuries. More than 25% of all of the injuries and fatalities in all industries in Hong Kong are caused by the construction industry. This is different from the U.S., whose top injury cause for fatal injuries is a fall to a lower level (34%) and nonfatal injuries (23%). The most frequent type of injuries in Hong Kong is due to lifting and carrying (19.2%). Recently, automated 3D sensing systems (Kinect) have been employed to identify motion-related hazards to improve construction safety conditions. However, limitations (such as extreme light conditions, occlusions and misrepresentations) of 3D sensing systems hinder its application in engineering practices. To resolve those limitations, this research proposed a coupled system which integrates and synchronizes the Kinect with Inertial Measurement Unit (IMUs). With the help of the coupled system, IMUs could uninterruptedly collect motion data (accelerations and angular rates), even under extreme light conditions or under occlusions, while Kinect could provide a reference system for IMUs to construct postures. The whole sensor network will be able to capture complete and reliable data even if Kinect fails to work properly. Moreover, the proposed coupled system will also promote other human related research, such as productivity and labor tracking.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".