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Advancements in Civil Engineering & Technology

Towards A Real-Time Data-Driven Approach for Proactive Injury Prevention in Construction

  • Open or Close Esther Obonyo1* and Junqi Zhao2

    1Engineering Design and Architectural Engineering, The Pennsylvania State University, USA

    2Architectural Engineering, The Pennsylvania State University, USA

    *Corresponding author: Esther Obonyo, Associate Professor, Engineering Design and Architectural Engineering, The Pennsylvania State University, University Park, PA, USA

Submission: August 17, 2018 Published: August 30, 2018

DOI: 10.31031/ACET.2018.02.000533

ISSN 2639-0574
Volume2 Issue2


There is a need for more proactive injury prevention strategies that can enhance the progress towards zero injuries and fatalities in construction. There is a specific focus on Musculoskeletal Disorders (MSD), which according to the National Institute for Occupational Safety and Health (NIOSH)’s definition is a “soft-tissue injuries caused by sudden or sustained exposure to repetitive motion, force, vibration, and awkward positions” that “affect the muscles, nerves, tendons, joints, and cartilage in the upper and lower limbs, neck and lower back. “There are some knowledge gaps impeding the design and development of intervention strategies. This paper presents preliminary findings from research directed at investigating the potential for addressing this need through leveraging emerging sensor-based technologies using real-time data. The proposed approach leverages emerging lowcost wearable sensing and advanced data analytics techniques. One of the key barriers to injury prevention is the lack of reliable data on the complex interactions across contributing factors.

Lessons can be learned from a “Web of Causation” approach that is being used to characterize and analyse the development progress of the disease. This is a data-intensive approach. It is the contention of this paper that the required data collection and processing needs can be addressed using emerging Wearable Technologies (WT). The collected motion data can be used to improve the detection of activities that have MSD-related risks and enhance the understanding of the nature of the risk. The output can be used to perform more robust quantitative assessments of risk factors. It can also be used to develop proactive strategies that can be used to minimize the occurrence of MSD.

Keywords: Construction safety; Injury prevention; Data analytics; Musculoskeletal disorders

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