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Idris Jeelani, Alex Albert, Kevin Han. Hazard Recognition – A visual Search Perspective. 60,000fatal injuries are experienced in construction industry world wide ...
Hazard Recognition – A visual Search Perspective Idris Jeelani, Alex Albert, Kevin Han North Carolina State University Introduction

Results

60,000 fatal injuries are experienced in construction industry world wide. 1 every 9 minutes!

R1

Recognizing hazards is the 1st step in safety management Adequate Safety Control

Physical Barrier Recognized

About 50% of hazards remain unrecognized!!

Procedural Barrier Inadequate Safety Control

Not Recognized

Why hazards remain unrecognized? Examine

Visual Search

Hazard Recognition

Hazard Recognition is a visual search Process

Scanpath: person A:

13 Reasons why hazards remain unrecognized

Hazards recognized: 31%

1. 2. 3. 4.

Selective attention Premature termination of search. Visually unperceivable Unassociated with primary task.

5. 6. 7. 8. 9. 10. 11. 12. 13.

Hazards perceived to impose risk. Low prevalence hazards. Unknown potential hazard set. Multiple hazards from single source Hazzard without immediate effect Task unfamiliarity. Operational unfamiliarity. Source detection failure. Latent hazards

R3

R2: What are the parameters of an

unrecognized?

efficient hazard Search?

Exploratory Study with workers

13 factors that lead to unrecognized hazards

Training

𝒄

Hazard Recognition

Training

2500

Eye Tracking overlay on 3D point cloud

2233 2145

Over 90 % accuracy achieved in computing fixations

2000

1453

1500

1355 1125 1028

1017 1000

930

500

𝒃

𝒄′

Longer Search Duration More Number of Fixations Wider Attentional Distribution Longer Fixation Duration

Hazards recognized: 73%

Visual search 𝒂

   

Scanpath: person B:

Method R1: Why do hazards remain

R2 Characteristics of efficient visual search

Hazard Recognition

0 1

2

𝒄 = 𝒄′ + 𝒂𝒃

3 Manual

4

System

Indirect Effect Validation study

Direct Effect Total Effect

Practical contribution Intervention improved performance by 43%

R3: How to automate eye tracking data analysis?

100%

90%

80%

Personalized hazard recognition Visual cues for systematic hazard recognition performance feedback Personalized Intervention to improve HR

% Hazards Recognized

Exploratory study with Experts

70%

60%

43% Improvement

50%

40%

30%

Before Training

20%

After Training

10%

Personalized eye-tracking Metacognitive prompts to encourage self-diagnosis and correction visual attention feedback

0% 0

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