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Predicting Mental Workload of Using Exoskeletons for Construction Work: A Deep Learning Approach

  • Abiola Akanmu
  • Adedeji Afolabi
  • Akinwale Okunola

Exoskeletons are gaining attention as a potential solution for addressing low back injury in the construction industry. However, use of active back-support exoskeletons in construction can trigger unintended consequences which could increase mental workload of users while working with exoskeletons. Prolonged increase in mental workload could impact workers’ wellbeing and productivity. Prediction of mental workload during exoskeleton-use could inform strategies to mitigate the triggers. This study investigates a machine-learning framework for predicting mental workload of workers while using active back-support exoskeletons for construction work. Laboratory experiments were conducted wherein Electroencephalography (EEG) data were collected from participants wearing active back-support exoskeletons to perform flooring task. The EEG data underwent preprocessing, including band filtering, notch filtering, and independent component analysis, to remove artifacts and ensure data quality. A regression-based Long Short-Term Memory network was trained to forecast future time steps of the processed EEG data. The performance of the network was evaluated using root mean square error (RMSE) and r-squared (R2). A RMSE of 0.1527 and R2 of 0.9665 indicating good fit and strong correlation, respectively, were observed between the predicted and actual EEG data. Results of the comparison between the actual and predicted mental workload also show strong correction with an R2 of 0.8692. The findings motivate research directions into real-time monitoring of mental workload of workers during exoskeleton-use. The study has significant implications for stakeholders, enabling them to gain a deeper understanding of the impact of mental workload while using exoskeletons thereby providing opportunities for mitigation

  • Keywords:
  • Work-related musculoskeletal disorders,
  • Exoskeleton,
  • Mental workload,
  • Electroencephalogram,
  • Long Short-Term Memory,
  • Flooring task,
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Abiola Akanmu

Virginia Tech/Myers Lawson School of Construction, United States - ORCID: 0000-0001-9145-4865

Adedeji Afolabi

Virginia Tech/Myers Lawson School of Construction, United States - ORCID: 0000-0002-9065-4766

Akinwale Okunola

Virginia Tech/Myers Lawson School of Construction, United States - ORCID: 0009-0002-5235-1307

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  • Publication Year: 2023
  • Pages: 691-700

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  • Publication Year: 2023

Chapter Information

Chapter Title

Predicting Mental Workload of Using Exoskeletons for Construction Work: A Deep Learning Approach

Authors

Abiola Akanmu, Adedeji Afolabi, Akinwale Okunola

DOI

10.36253/979-12-215-0289-3.69

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

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979-12-215-0289-3

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979-12-215-0257-2

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Proceedings e report

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2704-601X

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2704-5846

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