Validating Photoplethysmography (PPG) data for driver drowsiness detection

被引:11
|
作者
Amidei, Andrea [1 ]
Fallica, Piero G. [2 ]
Conoci, Sabrina [3 ]
Pavan, Paolo [1 ]
机构
[1] Univ Modena & Reggio Emilia, Dept Engn Enzo Ferrari, Modena, Italy
[2] Univ Messina, INSTM Dept Engn, Messina, Italy
[3] Univ Messina, Dept Chem Biol Pharmaceut & Environm Sci, Messina, Italy
关键词
driver drowsiness; Photoplethysmography; validation; PPG shape parameters; SYSTEM;
D O I
10.1109/MetroAutomotive50197.2021.9502865
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Drowsiness is one of the first casualty factors of car accidents. A large number of studies have been conducted to reduce the risk of car accidents and, many of them, are based on the detection of biological signals to determine driver drowsiness. In this way, several prototypes have been proposed but all of them are efficient in specific scenarios only. Photoplethysmography (PPG) is a non-invasive tool that allows monitoring heart activity, it is also used to evaluate driver drowsiness. This paper introduces a prototype based on PPG signals able to improve current systems in terms of evaluation time and results clearness. We performed a measurement campaign to compare experimental data with literature. The goal is to validate the prototype.
引用
收藏
页码:147 / 151
页数:5
相关论文
共 50 条
  • [21] An efficient approach for driver drowsiness detection at moderate drowsiness level based on electroencephalography signal and vehicle dynamics data
    Houshmand, Sara
    Kazemi, Reza
    Salmanzadeh, Hamed
    JOURNAL OF MEDICAL SIGNALS & SENSORS, 2022, 12 (04): : 294 - 305
  • [22] Data Fusion to Develop a Driver Drowsiness Detection System with Robustness to Signal Loss
    Samiee, Sajjad
    Azadi, Shahram
    Kazemi, Reza
    Nahvi, Ali
    Eichberger, Arno
    SENSORS, 2014, 14 (09) : 17832 - 17847
  • [23] DrowsyDetectNet: Driver Drowsiness Detection Using Lightweight CNN With Limited Training Data
    Venkateswarlu, Madduri
    Ch, Venkata Rami Reddy
    IEEE ACCESS, 2024, 12 : 110476 - 110491
  • [24] Fuzzy Decision Algorithm for Driver Drowsiness Detection
    Vesselenyi, Tiberiu
    Rus, Alexandru
    Mitran, Tudor
    Moca, Sorin
    Lehel, Csokmai
    30TH SIAR INTERNATIONAL CONGRESS OF AUTOMOTIVE AND TRANSPORT ENGINEERING: SCIENCE AND MANAGEMENT OF AUTOMOTIVE AND TRANSPORTATION ENGINEERING, 2020, : 458 - 467
  • [25] Leveraging Transfer Learning for Driver Drowsiness Detection
    Dwivedi, Subhash Arun
    Attry, Amit
    Singla, Kanika
    ADVANCES IN DATA AND INFORMATION SCIENCES, 2022, 318 : 603 - 611
  • [26] An embedded intelligence engine for driver drowsiness detection
    Vadlamudi, Shirisha
    Ahmadinia, Ali
    IET COMPUTERS AND DIGITAL TECHNIQUES, 2022, 16 (01): : 10 - 18
  • [27] Eye Based Drowsiness Detection System for Driver
    Prima Dewi Purnamasari
    Arie Kriswoyo
    Anak Agung Putri Ratna
    Dodi Sudiana
    Journal of Electrical Engineering & Technology, 2022, 17 : 697 - 705
  • [28] A Fuzzy Based Method for Driver Drowsiness Detection
    Rigane, Omar
    Abbes, Karim
    Abdelmoula, Chokri
    Masmoudi, Mohamed
    2017 IEEE/ACS 14TH INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA), 2017, : 143 - 147
  • [29] Assessment of a standalone photoplethysmography (PPG) algorithm for detection of atrial fibrillation on wristband-derived data
    Seldera, J. L.
    Proesmans, T.
    Breukel, L.
    Dur, O.
    Gielen, W.
    van Rossum, A. C.
    Allaart, C. P.
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2020, 197
  • [30] Neuromorphic Sensing for Yawn Detection in Driver Drowsiness
    Kielty, Paul
    Dilmaghani, Mehdi Sefidgar
    Ryan, Cian
    Lemley, Joe
    Corcoran, Peter
    FIFTEENTH INTERNATIONAL CONFERENCE ON MACHINE VISION, ICMV 2022, 2023, 12701