Evidence map›Paper›PMID 39686066›Full record

ArticleSensors (Basel, Switzerland)2024

An Identification Method for Road Hypnosis Based on the Fusion of Human Life Parameters.

Bin Wang, Jingheng Wang, Xiaoyuan Wang, Longfei Chen, Chenyang Jiao, Han Zhang, Yi Liu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Bin WangCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.
Jingheng WangDepartment of Mathematics, Ohio State University, Columbus, OH 43220, USA.
Xiaoyuan WangCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.ORCID 0000-0003-2418-7394
Longfei ChenCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.ORCID 0000-0002-0065-1001
Chenyang JiaoCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.
Han ZhangCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.
Yi LiuCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.

Funding

New Generation of Information Technology Innovation Project of China University Innovation Fund of Ministry of Education No. 2022IT191
6 · The paper itself

Abstract

A driver in road hypnosis has two different types of characteristics. One is the external characteristics, which are distinct and can be directly observed. The other is internal characteristics, which are indistinctive and cannot be directly observed. The eye movement characteristic, as a distinct external characteristic, is one of the typical characteristics of road hypnosis identification. The electroencephalogram (EEG) characteristic, as an internal feature, is a golden parameter of drivers' life identification. This paper proposes an identification method for road hypnosis based on the fusion of human life parameters. Eye movement data and EEG data are collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with principal component analysis (PCA) and independent component analysis (ICA), respectively. Eye movement data can be trained with a self-attention model (SAM), and the EEG data can be trained with the deep belief network (DBN). The road hypnosis identification model can be constructed by combining the two trained models with the stacking method. Repeated Random Subsampling Cross-Validation (RRSCV) is used to validate models. The results show that road hypnosis can be effectively recognized using the constructed model. This study is of great significance to reveal the essential characteristics and mechanisms of road hypnosis. The effectiveness and accuracy of road hypnosis identification can also be improved through this study.

Indexed as

Automobile DrivingElectroencephalographyHypnosisPrincipal Component AnalysisAdultAlgorithmsEye MovementsHumansMaledrivershuman life parametersroad hypnosisstate identificationvehicle

Identifiers

PMID39686066
PMCPMC11644267

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.