A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Accelerometer-based fall-portent detection algorithm for construction tiling operation
https://orcid.org/0000-0002-4437-5053
AbstractFall accidents are a major safety issue and a perennial problem in the construction industry. However, few studies have focused on detecting fall portents, identification of which may prevent falls from occurring. This study developed an accelerometer-based fall portent detection system that employed a hierarchical threshold-based algorithm. We designed tiling experiments to evaluate the performance of the proposed system. The participants performed the tasks under normal, inebriation, and sleepiness conditions on a scaffold while four accelerometers were attached to their chest, waist, arm, and hand. The results revealed that the traditional threshold-based algorithms had unacceptable accuracies of less than 30.66%. Most false warnings could be attributed to misidentifications of work-related motions. However, the work-related motions had a limited effect on the hierarchical threshold-based algorithm, which exhibited a satisfactory detection rate and accuracy of 76.86% and 79.13%, respectively.
HighlightsHierarchical threshold-based algorithms were developed and evaluated.Participants performed tiling under normal, inebriation, and sleepiness conditions.Hierarchical threshold-based algorithms significantly improved the accuracies.The algorithm had the highest detection rate (87.5%) in the sleepiness experiment.The algorithm had higher accuracies (89.17% & 79.13%) in the abnormal experiments.
Accelerometer-based fall-portent detection algorithm for construction tiling operation
https://orcid.org/0000-0002-4437-5053
AbstractFall accidents are a major safety issue and a perennial problem in the construction industry. However, few studies have focused on detecting fall portents, identification of which may prevent falls from occurring. This study developed an accelerometer-based fall portent detection system that employed a hierarchical threshold-based algorithm. We designed tiling experiments to evaluate the performance of the proposed system. The participants performed the tasks under normal, inebriation, and sleepiness conditions on a scaffold while four accelerometers were attached to their chest, waist, arm, and hand. The results revealed that the traditional threshold-based algorithms had unacceptable accuracies of less than 30.66%. Most false warnings could be attributed to misidentifications of work-related motions. However, the work-related motions had a limited effect on the hierarchical threshold-based algorithm, which exhibited a satisfactory detection rate and accuracy of 76.86% and 79.13%, respectively.
HighlightsHierarchical threshold-based algorithms were developed and evaluated.Participants performed tiling under normal, inebriation, and sleepiness conditions.Hierarchical threshold-based algorithms significantly improved the accuracies.The algorithm had the highest detection rate (87.5%) in the sleepiness experiment.The algorithm had higher accuracies (89.17% & 79.13%) in the abnormal experiments.
Accelerometer-based fall-portent detection algorithm for construction tiling operation
https://orcid.org/0000-0002-4437-5053
AbstractFall accidents are a major safety issue and a perennial problem in the construction industry. However, few studies have focused on detecting fall portents, identification of which may prevent falls from occurring. This study developed an accelerometer-based fall portent detection system that employed a hierarchical threshold-based algorithm. We designed tiling experiments to evaluate the performance of the proposed system. The participants performed the tasks under normal, inebriation, and sleepiness conditions on a scaffold while four accelerometers were attached to their chest, waist, arm, and hand. The results revealed that the traditional threshold-based algorithms had unacceptable accuracies of less than 30.66%. Most false warnings could be attributed to misidentifications of work-related motions. However, the work-related motions had a limited effect on the hierarchical threshold-based algorithm, which exhibited a satisfactory detection rate and accuracy of 76.86% and 79.13%, respectively.
HighlightsHierarchical threshold-based algorithms were developed and evaluated.Participants performed tiling under normal, inebriation, and sleepiness conditions.Hierarchical threshold-based algorithms significantly improved the accuracies.The algorithm had the highest detection rate (87.5%) in the sleepiness experiment.The algorithm had higher accuracies (89.17% & 79.13%) in the abnormal experiments.
Accelerometer-based fall-portent detection algorithm for construction tiling operation
Fang, Yi-Cho (author) / Dzeng, Ren-Jye (author)
Automation in Construction ; 84 ; 214-230
2017-09-13
17 pages
Article (Journal)
Electronic Resource
English
Accelerometer-based fall-portent detection algorithm for construction tiling operation
| British Library Online Contents | 2017
Fall-portent detection for construction sites based on computer vision and machine learning
| Emerald Group Publishing | 2025
Railtalk - Worrying portent for the national electrification programme
Online Contents | 2013
Research of Humen Fall Detection Algorithm Based on Tri-Axis Accelerometer
| British Library Online Contents | 2012
Smart Fall: Accelerometer-Based Fall Detection in a Smart Home Environment
| Springer Verlag | 2015