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Machine Learning for Occupational Slip-Trip-Fall Incidents Classification Within Commercial Grain Elevators

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Advances in Safety Management and Human Performance (AHFE 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 262))

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Abstract

The grain handling industry plays a significant role in U.S. agriculture by storing, distributing, and processing a variety of agricultural commodities. Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to severe injuries, due to the nature of the activities and workplace. One of the leading causes of occupational incidents in all industries, including grain elevators, is slip, trip, and fall (STF). Therefore, prediction of STF incidents prior to occurrence is significant in occupational safety analysis. Despite high frequency of STF incidents at work, exploring their dominant factors via machine learning algorithms in agro-manufacturing environments is relatively new or unaddressed. Safety professionals may utilize the prediction and analysis of determinant factors of occupational incidents for actionable prevention and safety mitigation planning and practices. The objective of this research is to describe the slip-trip-fall (STF) injuries and trends in a population of agribusiness operations workers within commercial grain elevators in the Midwest of the United States, identify risk factors for STF injuries, and develop prevention strategies for STF hazards.

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Authors and Affiliations

  1. Department of Technology, San Jose State University, San Jose, CA, 95192, USA

    Fatemeh Davoudi Kakhki

  2. Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA, 50011, USA

    Steven A. Freeman & Gretchen A. Mosher

Authors
  1. Fatemeh Davoudi Kakhki
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  2. Steven A. Freeman
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  3. Gretchen A. Mosher
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Correspondence to Fatemeh Davoudi Kakhki .

Editor information

Editors and Affiliations

  1. DPS, School of Engineering, University of Minho, Guimarães, Portugal

    Pedro M. Arezes

  2. Idaho National Laboratory, Idaho Falls, ID, USA

    Ronald L. Boring

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Davoudi Kakhki, F., Freeman, S.A., Mosher, G.A. (2021). Machine Learning for Occupational Slip-Trip-Fall Incidents Classification Within Commercial Grain Elevators. In: Arezes, P.M., Boring, R.L. (eds) Advances in Safety Management and Human Performance. AHFE 2021. Lecture Notes in Networks and Systems, vol 262. Springer, Cham. https://doi.org/10.1007/978-3-030-80288-2_18

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  • DOI: https://doi.org/10.1007/978-3-030-80288-2_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-80287-5

  • Online ISBN: 978-3-030-80288-2

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