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Assessing exposure to slip, trip, and fall hazards based on abnormal gait patterns predicted from confidence interval estimation
Cited 8 time in
Web of Science
Cited 10 time in Scopus
- Authors
- Issue Date
- 2022-07
- Publisher
- Elsevier BV
- Citation
- Automation in Construction, Vol.139, p. 104253
- Abstract
- Monitoring workers' exposures to slip, trip, and fall (STF) hazards is critical to preventing STFs at construction sites. This study developed a model to assess workers' exposures to STF hazards by predicting abnormal gait patterns from a series of steps. The model was then evaluated and validated through a field experiment. Gait variability features were extracted from a waist-worn inertial measurement unit (IMU) and converted into Mahalanobis distance. Bidirectional long short-term memory models were used to predict abnormal gait patterns using confidence interval estimation. The model generated an Unweighted Average Recall (UAR) of 93.0% (normal walking: 93.0% and exposure to STF hazards: 93.0%), which demonstrates that workers' exposures to STF hazards can be continuously and remotely monitored, potentially helping to prevent STFs on construction worksites.
- ISSN
- 0926-5805
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- There are no files associated with this item.
Related Researcher
- College of Engineering
- Department of Architecture & Architectural Engineering
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