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Advances in Informatics and Computing in Civil and Construction Engineering pp 207–214Cite as

Detecting Falls-from-Height with Wearable Sensors and Reducing Consequences of Occupational Fall Accidents Leveraging IoT

Abstract

Labor intensive and hazardous nature of the construction activities plays an important role on the increase of the amount of accidents and fatalities on sites. One of the most important sources of fatalities occurring on construction sites is falls-from-height (FFH). Despite the various efforts for the solution over decades, the yearly statistics still indicate high amount of fatalities and severe injuries due to FFH accidents on construction sites. Medical literature emphasize that the time passed after the accident is a critical factor for avoiding preventable deaths and permanent disabilities of trauma patients. The objective of this study is to timely detect FFH accidents on construction sites by using a wearable device and to provide a real-time notification to the emergency medical team (EMT) leveraging Internet-of-Things (IoT). This is expected to maintain the earliest possible medical intervention on site in order to help reducing fatal and severe consequences of FFH accidents for construction workers. Test results of the system evaluation show that the fall is detected correctly and the alert message is sent to the prescribed addresses with 100% sensitivity. The system has shown a good accuracy for true detection of the fall height with an overall error rate of 10.8%. Another metric which shows the detection of the disconnected network time of the system has been surveyed and the results are accurate with an overall error rate of 3.16%.

Keywords

  • Occupational health and safety
  • Falls-From-Height
  • Internet of things (IoT)
  • Wearable sensors and devices
  • Data acquisition

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Authors and Affiliations

  1. Department of Civil Engineering, Middle East Technical University, Ankara, Turkey

    Onur Dogan & Asli Akcamete

Authors
  1. Onur Dogan
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  2. Asli Akcamete
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Correspondence to Onur Dogan .

Editor information

Editors and Affiliations

  1. CAEE, Illinois Institute of Technology, Chicago, IL, USA

    Dr. Ivan Mutis

  2. Institute of Civil Engineering, TU Berlin, Berlin, Germany

    Prof. Dr. Timo Hartmann

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Dogan, O., Akcamete, A. (2019). Detecting Falls-from-Height with Wearable Sensors and Reducing Consequences of Occupational Fall Accidents Leveraging IoT. In: Mutis, I., Hartmann, T. (eds) Advances in Informatics and Computing in Civil and Construction Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-00220-6_25

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  • DOI: https://doi.org/10.1007/978-3-030-00220-6_25

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