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Smart Glasses-Based Personnel Proximity Warning System for Improving Pedestrian Safety in Construction and Mining Sites

Author

Listed:
  • Jieun Baek

    (Department of Energy Resources Engineering, Pukyong National University, Busan 48513, Korea)

  • Yosoon Choi

    (Department of Energy Resources Engineering, Pukyong National University, Busan 48513, Korea)

Abstract

A smart glasses-based wearable personnel proximity warning system (PWS) was developed for pedestrian safety in construction and mining sites. The smart glasses receive signals transmitted by Bluetooth beacons attached to heavy equipment or vehicles, with the proximity determined by the signal strength. A visual alert is displayed to the wearer when in close proximity. The media access control address of the Bluetooth beacon provides information on the approaching equipment or vehicle, which is displayed to the wearer so that they can respond appropriately. There was a detection distance of at least 10 m regardless of the direction the pedestrian was looking and the alert was successful in all 40 trials at ≥10 meters. The subjective workload was evaluated using the NASA task load index on ten subjects, either without a personal PWS, with a smartphone-based PWS, or with the smart glasses-based PWS. The mental, temporal, and physical stresses were lowest when using the smart glasses-based PWS. Smart glasses-based PWSs can improve work efficiency by freeing both hands of the pedestrians, and various functions can be supported through application development. Therefore, they are particularly useful for pedestrian safety in construction and mining sites.

Suggested Citation

  • Jieun Baek & Yosoon Choi, 2020. "Smart Glasses-Based Personnel Proximity Warning System for Improving Pedestrian Safety in Construction and Mining Sites," IJERPH, MDPI, vol. 17(4), pages 1-15, February.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:4:p:1422-:d:323989
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    References listed on IDEAS

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    1. Eric Daniel Marks & Jochen Teizer, 2013. "Method for testing proximity detection and alert technology for safe construction equipment operation," Construction Management and Economics, Taylor & Francis Journals, vol. 31(6), pages 636-646, June.
    2. Byung-Wan Jo & Yun-Sung Lee & Jung-Hoon Kim & Do-Keun Kim & Pyung-Ho Choi, 2017. "Proximity Warning and Excavator Control System for Prevention of Collision Accidents," Sustainability, MDPI, vol. 9(8), pages 1-20, August.
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    Citations

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    Cited by:

    1. Yeanjae Kim & Yosoon Choi, 2022. "Smart Helmet-Based Proximity Warning System to Improve Occupational Safety on the Road Using Image Sensor and Artificial Intelligence," IJERPH, MDPI, vol. 19(23), pages 1-15, December.
    2. Zheng Zhu & Jingfeng Yuan & Qiuhu Shao & Lei Zhang & Guangqi Wang & Xuewei Li, 2020. "Developing Key Safety Management Factors for Construction Projects in China: A Resilience Perspective," IJERPH, MDPI, vol. 17(17), pages 1-20, August.
    3. Daniel Salinas & Felipe Muñoz-La Rivera & Javier Mora-Serrano, 2022. "Critical Analysis of the Evaluation Methods of Extended Reality (XR) Experiences for Construction Safety," IJERPH, MDPI, vol. 19(22), pages 1-26, November.

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