A hybrid approach for driver drowsiness detection utilizing practical data to improve performance system and applicability.
BACKGROUND: Numerous systems for detecting driver drowsiness have been developed; however, these systems have not yet been widely used in real-time. OBJECTIVE: The purpose of this study was to investigate at the feasibility of detecting alert and drowsy states in drivers using an integration of feat...
| Publicado en: | Work Vol. 77; no. 4; pp. 1165 - 1178 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas forms pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=176591142&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 176591142 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10519815 3RC jtl: Work issn: 10519815 maglogo: N pubinfo: dt: 2024 vid: 77 iid: 4 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 176591142 173730231 176591142 176591142 10.3233/WOR-230179 176591142 ppf: 1165 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A hybrid approach for driver drowsiness detection utilizing practical data to improve performance system and applicability. aug: au: Khanehshenas, Farin Mazloumi, Adel Nahvi, Ali Nickabadi, Ahmad Sadeghniiat, Khosro Rahimiforoushani, Abbas Aghamalizadeh, Alireza affil: Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran sug: subj: Automobile Driving Psychosocial Factors Sleep Stages Wearable Sensors Utilization Computer Simulation Utilization Support Vector Machine Utilization Neural Networks (Computer) Utilization Decision Trees Utilization Accidents, Traffic Prevention and Control Human Male Young Adult Adult Experimental Studies Machine Learning Descriptive Statistics Comparative Studies Data Analysis Software Regression Questionnaires kappa Statistic Internal Consistency Coefficient alpha Scales Circadian Rhythm Mann-Whitney U Test Pearson's Correlation Coefficient Respiratory Mechanics Reaction Time Wakefulness Pilot Studies Memory, Short Term Electrical Equipment and Supplies Multilayer Perceptrons Adult: 19-44 years Male ab: BACKGROUND: Numerous systems for detecting driver drowsiness have been developed; however, these systems have not yet been widely used in real-time. OBJECTIVE: The purpose of this study was to investigate at the feasibility of detecting alert and drowsy states in drivers using an integration of features from respiratory signals, vehicle lateral position, and reaction time and out-of-vehicle ways of data collection in order to improve the system's performance and applicability in the real world. METHODS: Data was collected from 25 healthy volunteers in a driving simulator-based study. Their respiratory activity was recorded using a wearable belt and their reaction time and vehicle lateral position were measured using tests developed on the driving simulator. To induce drowsiness, a monotonous driving environment was used. Different time domain features have been extracted from respiratory signals and combined with the reaction time and lateral position of the vehicle for modeling. The observer of rating drowsiness (ORD) scale was used to label the driver's actual states. The t-tests and Man-Whitney test was used to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features then combined to investigate the improvement in performance using the Multilayer Perceptron (MLP), the Support Vector Machines (SVMs), the Decision Trees (DTs), and the Long Short Term Memory (LSTM) classifiers. The models were implemented in Python library 3.6. RESULTS: The experimental results illustrate that the support vector machine classifier achieved accuracy of 88%, precision of 85%, recall of 83%, and F1 score of 84% using selected features. CONCLUSION: These results indicate the possibility of very accurate detection of driver drowsiness and a viable solution for a practical driver drowsiness system based on combined measurement using less-intrusive and out-of-vehicle recording methods. pubtype: Academic Journal doctype: equations & formulas forms pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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