Classification and Recognition of Operators’ Mental Load Under Repetitive High-precision Tasks Based on BP Neural Network
- DOI
- 10.2991/assehr.k.210806.067How to use a DOI?
- Keywords
- Repetitive high-precision tasks, mental load, Oxyhaemoglobin (O2Hb), BP neural network
- Abstract
In order to identify the mental load of operators under repetitive high-precision tasks effectively, 36 subjects were recruited in this study. The fine tasks on the electronic assembly line were simulated in the laboratory, and operators’ behavioural performance (completion time, error rate) and the changes of Oxyhaemoglobin (O2Hb) in prefrontal channels 2, 8, 12, 17, 20 and 21 of the brain were characteristic factors for mental load recognition. A model of recognition of mental load state of operators based on BP neural network was constructed and the fatigue state of operators was divided into four levels. Finally, the recognition rate of the mental load state of operators was 86.81% by combining the experimental data. The combination of behavioural performance indicators and physiological measurement indicators can effectively identify mental work state of operators, which provides a new idea for classification and recognition of mental load state of operators under repetitive high-precision operation tasks, and provides a reference for the establishment of effective labour organization.
- Copyright
- © 2021, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Wenmin Han AU - Huimin Qiang AU - Peiyao Li PY - 2021 DA - 2021/08/09 TI - Classification and Recognition of Operators’ Mental Load Under Repetitive High-precision Tasks Based on BP Neural Network BT - Proceedings of the 2021 5th International Seminar on Education, Management and Social Sciences (ISEMSS 2021) PB - Atlantis Press SP - 357 EP - 361 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.210806.067 DO - 10.2991/assehr.k.210806.067 ID - Han2021 ER -