Recognizing the situation awareness of forklift operators based on EEG techniques in a field experiment
Lack of situation awareness (SA) is the primary cause of human errors when operating forklifts, so determining the SA level of the forklift operator is crucial to the safety of forklift operations. An EEG recognition approach of forklift operator SA in actual settings was presented in order to addre...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2024.1323190/full |
| _version_ | 1797302458791231488 |
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| author | Xin Li Xin Li Yutao Kang Weijiong Chen Feng Liu Yu Jiao Yabin Luo |
| author_facet | Xin Li Xin Li Yutao Kang Weijiong Chen Feng Liu Yu Jiao Yabin Luo |
| author_sort | Xin Li |
| collection | DOAJ |
| description | Lack of situation awareness (SA) is the primary cause of human errors when operating forklifts, so determining the SA level of the forklift operator is crucial to the safety of forklift operations. An EEG recognition approach of forklift operator SA in actual settings was presented in order to address the issues with invasiveness, subjectivity, and intermittency of existing measuring methods. In this paper, we conducted a field experiment that mimicked a typical forklift operation scenario to verify the differences in EEG states of forklift operators with different SA levels and investigate the correlation of multi-band combination features of each brain region of forklift operators with SA. Based on the sensitive EEG combination indexes, Support Vector Mechanism was used to construct a forklift operator SA recognition model. The results revealed that there were differences between forklift operators with high and low SA in the θ, α, and β frequency bands in zones F, C, P, and O; combined EEG indicators θ/β, (α + θ)/(α + β), and θ/(α + β) in zones F, P, and C were significantly correlated with SA; the recognition accuracy of the model reached 88.64% in the case of combined EEG indicators of zones C & F & P as input. It could provide a reference for SA measurement, contributing to the improvement of SA. |
| first_indexed | 2024-03-07T23:38:07Z |
| format | Article |
| id | doaj.art-295f90d160fd45be8c86d98da282340e |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-07T23:38:07Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-295f90d160fd45be8c86d98da282340e2024-02-20T04:42:51ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2024-02-011810.3389/fnins.2024.13231901323190Recognizing the situation awareness of forklift operators based on EEG techniques in a field experimentXin Li0Xin Li1Yutao Kang2Weijiong Chen3Feng Liu4Yu Jiao5Yabin Luo6College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, ChinaCOSCO SHIPPING Heavy Industry Co., Ltd., Shanghai, ChinaCollege of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, ChinaMerchant Marine College, Shanghai Maritime University, Shanghai, ChinaCollege of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, ChinaCollege of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, ChinaCollege of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, ChinaLack of situation awareness (SA) is the primary cause of human errors when operating forklifts, so determining the SA level of the forklift operator is crucial to the safety of forklift operations. An EEG recognition approach of forklift operator SA in actual settings was presented in order to address the issues with invasiveness, subjectivity, and intermittency of existing measuring methods. In this paper, we conducted a field experiment that mimicked a typical forklift operation scenario to verify the differences in EEG states of forklift operators with different SA levels and investigate the correlation of multi-band combination features of each brain region of forklift operators with SA. Based on the sensitive EEG combination indexes, Support Vector Mechanism was used to construct a forklift operator SA recognition model. The results revealed that there were differences between forklift operators with high and low SA in the θ, α, and β frequency bands in zones F, C, P, and O; combined EEG indicators θ/β, (α + θ)/(α + β), and θ/(α + β) in zones F, P, and C were significantly correlated with SA; the recognition accuracy of the model reached 88.64% in the case of combined EEG indicators of zones C & F & P as input. It could provide a reference for SA measurement, contributing to the improvement of SA.https://www.frontiersin.org/articles/10.3389/fnins.2024.1323190/fullsituation awarenessEEGcorrelationrecognitionforklift operators |
| spellingShingle | Xin Li Xin Li Yutao Kang Weijiong Chen Feng Liu Yu Jiao Yabin Luo Recognizing the situation awareness of forklift operators based on EEG techniques in a field experiment Frontiers in Neuroscience situation awareness EEG correlation recognition forklift operators |
| title | Recognizing the situation awareness of forklift operators based on EEG techniques in a field experiment |
| title_full | Recognizing the situation awareness of forklift operators based on EEG techniques in a field experiment |
| title_fullStr | Recognizing the situation awareness of forklift operators based on EEG techniques in a field experiment |
| title_full_unstemmed | Recognizing the situation awareness of forklift operators based on EEG techniques in a field experiment |
| title_short | Recognizing the situation awareness of forklift operators based on EEG techniques in a field experiment |
| title_sort | recognizing the situation awareness of forklift operators based on eeg techniques in a field experiment |
| topic | situation awareness EEG correlation recognition forklift operators |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2024.1323190/full |
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