Detecting falls-from-height with wearable sensors and reducing the consequences of occupational fall accidents leveraging Internet-of-Things

2019
Dogan, Onur
Hazardous and labor-intensive nature of the construction industry has a prominent impact on increasing the number of occupational accidents and fatalities in construction. Falls-from-height (FFH) is one of the most important sources of these fatalities. Despite many valuable prevention strategies and efforts implemented against occupational fall accidents on construction sites, the fatality rate records do not indicate a significant decrease. In medical literature, the time passed after the accident is critical to avoid preventable deaths and permanent disabilities of trauma patients. By combining these, a novel approach is exhibited to timely detect FFH accidents on construction sites using a wearable device to provide emergency medical team (EMT) with real-time notification including the height of fall and the time of fall information by leveraging Internet-of-Things (IoT). It is aimed to maintain the earliest possible medical intervention to the victim on site to help reducing severe and fatal consequences of FFH accidents for construction workers. A wearable system that can be used by construction workers on site has been developed and tested against FFH on construction sites using dummies. The experiments have shown promising results with 100% successful detection of FFH accidents by having an overall error rate of 5,8% in the calculation of the accident fall height. In order to detect FFH time correctly, an additional metric that shows the detection of the disconnected network time of the system has been investigated and the results are accurate with an overall error rate of 3.16%. Additional tests have also been conducted for the validation of the system against false positives on construction sites and none of the experiments produced false alarms during tests.

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Citation Formats
O. Dogan, “Detecting falls-from-height with wearable sensors and reducing the consequences of occupational fall accidents leveraging Internet-of-Things,” M.S. - Master of Science, Middle East Technical University, 2019.