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A Deep-Learning Based Worker’s Pose Estimation

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Frontiers of Computer Vision (IW-FCV 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1212))

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Abstract

Work in a factory is physically demanding. It requires workers to perform tasks in different awkward positions. Thus, long work shifts might have prolonged effects on workers’ physical health. To minimize the risks, we introduce an automatic workers’ pose estimation system, which will calculate a worker’s body angle and indicate which angles are safe or not safe for performing tasks in various work places. By combining CMU OpenPose with body assessment tools, such as Rapid Entire Body Assessment (REBA) and Rapid Upper Limb Assessment (RULA), the proposed system automatically determines a worker’s risk pose. This method, intended to replace a manual analysis of work posture, will help build safer environments for workers.

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Acknowledgement

This research was supported by Basic Science Research Program through the National research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A1B07047936).

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Authors and Affiliations

  1. Mokpo National University, Mokpo, Cheonggye-Myeon, South Korea

    Prabesh Paudel & Kyoung-Ho Choi

Authors
  1. Prabesh Paudel
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  2. Kyoung-Ho Choi
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Correspondence to Prabesh Paudel .

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Editors and Affiliations

  1. Saitama Institute of Technology, Saitama, Japan

    Wataru Ohyama

  2. Kyungpook National University, Daegu, Korea (Republic of)

    Soon Ki Jung

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Paudel, P., Choi, KH. (2020). A Deep-Learning Based Worker’s Pose Estimation. In: Ohyama, W., Jung, S. (eds) Frontiers of Computer Vision. IW-FCV 2020. Communications in Computer and Information Science, vol 1212. Springer, Singapore. https://doi.org/10.1007/978-981-15-4818-5_10

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  • DOI: https://doi.org/10.1007/978-981-15-4818-5_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4817-8

  • Online ISBN: 978-981-15-4818-5

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