Accelerometer-based fall-portent detection algorithm for construction tiling operation

- Hierarchical 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.

Fall 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.