Real-time fatigue monitoring system for motorcycle riders using sensors and a random forest algorithm.
Fatigue is a major determinant of motorcycle accidents, posing a critical threat to road safety by impairing riders' psychophysiological performance and increasing the crash risk. This study introduces a real-time fatigue monitoring system specifically designed for motorcycle riders, integrating phy...
| Publicado en: | International Journal of Occupational Safety & Ergonomics Vol. 32; no. 2; pp. 561 - 572 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas pictorial research tables/charts tracings Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jun2026
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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=194165284&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194165284 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10803548 39QK jtl: International Journal of Occupational Safety & Ergonomics issn: 10803548 maglogo: N pubinfo: dt: Jun2026 vid: 32 iid: 2 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 194165284 188387409 194165284 194165284 10.1080/10803548.2025.2556605 194165284 ppf: 561 ppct: 11 formats: tig: atl: Real-time fatigue monitoring system for motorcycle riders using sensors and a random forest algorithm. aug: au: Soenandi, Iwan Aang Widodo, Lamto Hayat, Cynthia Harsono, Budi Eratus Siswahono, Sinode Jonsmith Djaha, Rymartin affil: Department of Industrial Engineering, Krida Wacana Christian University, Indonesia sug: subj: Health Information Systems Monitoring, Physiologic Fatigue Psychosocial Factors Electrical Equipment and Supplies Motor Vehicles Random Forest Machine Learning Human Male Female Adult Body Mass Index Heart Rate Variability Skin Physiology Biological Markers Electrocardiography ROC Curve Descriptive Statistics Data Analysis Software Funding Source Adult: 19-44 years Male Female ab: Fatigue is a major determinant of motorcycle accidents, posing a critical threat to road safety by impairing riders' psychophysiological performance and increasing the crash risk. This study introduces a real-time fatigue monitoring system specifically designed for motorcycle riders, integrating physiological signal acquisition with machine learning classification. Heart rate variability (HRV) and galvanic skin response (GSR) sensors, established biomarkers of fatigue, were employed to continuously collect physiological data. The acquired signals underwent pre-processing to minimize noise and artefacts, followed by extraction of key features, including time-domain HRV indices and GSR conductance levels. These features were classified using a random forest algorithm, selected for robustness and accuracy in high-dimensional data contexts. The system discriminates fatigued from non-fatigued states in real time and delivers multimodal alerts to promote timely rest. Validation through laboratory and field trials demonstrated 84% accuracy, underscoring the potential of physiological monitoring with machine learning to enhance motorcycle rider safety. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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