Experimental Study Of Car Drivers Fatigue Detection System Based On Embedded Fuzzy System

Oyashi Bunge Niate; Achmad Arifin; Fauzan Arrofiqi

doi:10.1109/CENIM67940.2025.11325822

Experimental Study Of Car Drivers Fatigue Detection System Based On Embedded Fuzzy System

Oyashi Bunge Niate^*
, Achmad Arifin
, Fauzan Arrofiqi

^*Corresponding author for this work

Laboratory of BioCybernetics

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Traffic accidents in Indonesia increased throughout 2023, with 94.71% caused by driver negligence, predominantly due to fatigue. This study developed a driver drowsiness detection system based on embedded fuzzy system, integrated three variables, subject variables, vehicle variables, and environmental variables. The system employed master-slave architecture with STM32F411CEU6 and Arduino Uno as slaves, and Raspberry Pi as master. Hierarchical Fuzzy System improved computational efficiency with lower-level fuzzy classifying fatigue levels and driving safety, while the upper-level fuzzy on integrated results for final driving safety classification. The results showed reliable ECG signal quality (12.71 dB SNR), effective QRS detection using Pan-Tompkins algorithm, and stable GPS data with Kalman filtering. Simulation experiment achieved 70.8% accuracy, and real vehicle experiment achieved 83.3% accuracy. Experiments revealed that system has detection bias in transition fatigue conditions (Drowsy1 and Drowsy2). OBD-II compatibility issues were encountered. As solution, modification utilizing GPS for driving behavior analysis was implemented.

Original language	English
Title of host publication	Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia 2025, CENIM 2025
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	490-495
Number of pages	6
ISBN (Electronic)	9798331578541
DOIs	https://doi.org/10.1109/CENIM67940.2025.11325822
Publication status	Published - 2025
Event	6th International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2025 - Surabaya, Indonesia Duration: 25 Nov 2025 → 26 Nov 2025

Publication series

Name	Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia 2025, CENIM 2025

Conference

Conference	6th International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2025
Country/Territory	Indonesia
City	Surabaya
Period	25/11/25 → 26/11/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Driver fatigue detection
ECG
embedded system
hierarchical fuzzy decision support system
safety driving

Access to Document

10.1109/CENIM67940.2025.11325822

Cite this

Niate, O. B., Arifin, A., & Arrofiqi, F. (2025). Experimental Study Of Car Drivers Fatigue Detection System Based On Embedded Fuzzy System. In Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia 2025, CENIM 2025 (pp. 490-495). (Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia 2025, CENIM 2025). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CENIM67940.2025.11325822

Niate, Oyashi Bunge ; Arifin, Achmad ; Arrofiqi, Fauzan. / Experimental Study Of Car Drivers Fatigue Detection System Based On Embedded Fuzzy System. Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia 2025, CENIM 2025. Institute of Electrical and Electronics Engineers Inc., 2025. pp. 490-495 (Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia 2025, CENIM 2025).

@inproceedings{6832fa15ab4f41209c24d943a1f88493,

title = "Experimental Study Of Car Drivers Fatigue Detection System Based On Embedded Fuzzy System",

abstract = "Traffic accidents in Indonesia increased throughout 2023, with 94.71\% caused by driver negligence, predominantly due to fatigue. This study developed a driver drowsiness detection system based on embedded fuzzy system, integrated three variables, subject variables, vehicle variables, and environmental variables. The system employed master-slave architecture with STM32F411CEU6 and Arduino Uno as slaves, and Raspberry Pi as master. Hierarchical Fuzzy System improved computational efficiency with lower-level fuzzy classifying fatigue levels and driving safety, while the upper-level fuzzy on integrated results for final driving safety classification. The results showed reliable ECG signal quality (12.71 dB SNR), effective QRS detection using Pan-Tompkins algorithm, and stable GPS data with Kalman filtering. Simulation experiment achieved 70.8\% accuracy, and real vehicle experiment achieved 83.3\% accuracy. Experiments revealed that system has detection bias in transition fatigue conditions (Drowsy1 and Drowsy2). OBD-II compatibility issues were encountered. As solution, modification utilizing GPS for driving behavior analysis was implemented.",

keywords = "Driver fatigue detection, ECG, embedded system, hierarchical fuzzy decision support system, safety driving",

author = "Niate, \{Oyashi Bunge\} and Achmad Arifin and Fauzan Arrofiqi",

note = "Publisher Copyright: {\textcopyright}2025 IEEE.; 6th International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2025 ; Conference date: 25-11-2025 Through 26-11-2025",

year = "2025",

doi = "10.1109/CENIM67940.2025.11325822",

language = "English",

series = "Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia 2025, CENIM 2025",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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address = "United States",

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Niate, OB, Arifin, A & Arrofiqi, F 2025, Experimental Study Of Car Drivers Fatigue Detection System Based On Embedded Fuzzy System. in Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia 2025, CENIM 2025. Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia 2025, CENIM 2025, Institute of Electrical and Electronics Engineers Inc., pp. 490-495, 6th International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2025, Surabaya, Indonesia, 25/11/25. https://doi.org/10.1109/CENIM67940.2025.11325822

Experimental Study Of Car Drivers Fatigue Detection System Based On Embedded Fuzzy System. / Niate, Oyashi Bunge; Arifin, Achmad ; Arrofiqi, Fauzan.
Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia 2025, CENIM 2025. Institute of Electrical and Electronics Engineers Inc., 2025. p. 490-495 (Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia 2025, CENIM 2025).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Experimental Study Of Car Drivers Fatigue Detection System Based On Embedded Fuzzy System

AU - Niate, Oyashi Bunge

AU - Arifin, Achmad

AU - Arrofiqi, Fauzan

PY - 2025

Y1 - 2025

N2 - Traffic accidents in Indonesia increased throughout 2023, with 94.71% caused by driver negligence, predominantly due to fatigue. This study developed a driver drowsiness detection system based on embedded fuzzy system, integrated three variables, subject variables, vehicle variables, and environmental variables. The system employed master-slave architecture with STM32F411CEU6 and Arduino Uno as slaves, and Raspberry Pi as master. Hierarchical Fuzzy System improved computational efficiency with lower-level fuzzy classifying fatigue levels and driving safety, while the upper-level fuzzy on integrated results for final driving safety classification. The results showed reliable ECG signal quality (12.71 dB SNR), effective QRS detection using Pan-Tompkins algorithm, and stable GPS data with Kalman filtering. Simulation experiment achieved 70.8% accuracy, and real vehicle experiment achieved 83.3% accuracy. Experiments revealed that system has detection bias in transition fatigue conditions (Drowsy1 and Drowsy2). OBD-II compatibility issues were encountered. As solution, modification utilizing GPS for driving behavior analysis was implemented.

AB - Traffic accidents in Indonesia increased throughout 2023, with 94.71% caused by driver negligence, predominantly due to fatigue. This study developed a driver drowsiness detection system based on embedded fuzzy system, integrated three variables, subject variables, vehicle variables, and environmental variables. The system employed master-slave architecture with STM32F411CEU6 and Arduino Uno as slaves, and Raspberry Pi as master. Hierarchical Fuzzy System improved computational efficiency with lower-level fuzzy classifying fatigue levels and driving safety, while the upper-level fuzzy on integrated results for final driving safety classification. The results showed reliable ECG signal quality (12.71 dB SNR), effective QRS detection using Pan-Tompkins algorithm, and stable GPS data with Kalman filtering. Simulation experiment achieved 70.8% accuracy, and real vehicle experiment achieved 83.3% accuracy. Experiments revealed that system has detection bias in transition fatigue conditions (Drowsy1 and Drowsy2). OBD-II compatibility issues were encountered. As solution, modification utilizing GPS for driving behavior analysis was implemented.

KW - Driver fatigue detection

KW - ECG

KW - embedded system

KW - hierarchical fuzzy decision support system

KW - safety driving

UR - https://www.scopus.com/pages/publications/105033145492

U2 - 10.1109/CENIM67940.2025.11325822

DO - 10.1109/CENIM67940.2025.11325822

M3 - Conference contribution

AN - SCOPUS:105033145492

T3 - Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia 2025, CENIM 2025

SP - 490

EP - 495

BT - Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia 2025, CENIM 2025

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2025

Y2 - 25 November 2025 through 26 November 2025

ER -

Niate OB, Arifin A , Arrofiqi F. Experimental Study Of Car Drivers Fatigue Detection System Based On Embedded Fuzzy System. In Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia 2025, CENIM 2025. Institute of Electrical and Electronics Engineers Inc. 2025. p. 490-495. (Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia 2025, CENIM 2025). doi: 10.1109/CENIM67940.2025.11325822

Experimental Study Of Car Drivers Fatigue Detection System Based On Embedded Fuzzy System

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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