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Ergonomic Assessment with a Convolutional Neural Network. A Case Study with OWAS

  • 2021
  • OriginalPaper
  • Chapter
Published in:
Authors
Helios De Rosario
Enrique Medina-Ripoll
José Francisco Pedrero-Sánchez
Mercedes Sanchís-Almenara
Albert Valls-Molist
Pedro Pablo Miralles-Garcera
Published in
Proceedings of the 8th International Ergonomics Conference
Publisher
Springer International Publishing

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Abstract

The chapter delves into the application of a convolutional neural network for ergonomic risk assessment, focusing on the OWAS method. It highlights the high prevalence of musculoskeletal disorders among workers and the need for more effective risk assessment procedures. The authors introduce a web application that automates the detection of postures from video footage, significantly reducing the time required for evaluations. The tool's efficacy is demonstrated through a case study using samples from the Carnegie Mellon University Motion Capture Database. The results show a high level of accuracy in posture detection, with processing speeds capable of handling large volumes of data efficiently. The chapter discusses the potential for extending this approach to other ergonomic assessment methods, emphasizing the potential to reduce the incidence of musculoskeletal disorders in workplaces through faster and more objective evaluations.
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previous chapter Achilles Tendon Load at Squatting Working Position
next chapter Space, Time and Ergonomic Assessment of Order Picking Using Horizontal Carousel
Title
Ergonomic Assessment with a Convolutional Neural Network. A Case Study with OWAS
Authors
Helios De Rosario
Enrique Medina-Ripoll
José Francisco Pedrero-Sánchez
Mercedes Sanchís-Almenara
Albert Valls-Molist
Pedro Pablo Miralles-Garcera
Copyright Year
2021
Book
Proceedings of the 8th International Ergonomics Conference

Print ISBN: 978-3-030-66936-2

Electronic ISBN: 978-3-030-66937-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-66937-9_8
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