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Performance and Acceptance Evaluation of a Driver Drowsiness Detection System based on Smart Wearables

Published: 20 September 2021 Publication History

Abstract

Current systems for driver drowsiness detection often use driving-related parameters. Automated driving reduces the availability of these parameters. Techniques based on physiological signals seem to be a promising alternative. However, in a dynamic driving environment, only non- or minimal intrusive methods are accepted. In this work, a driver drowsiness detection system based on a smart wearable is proposed. A mobile application with an integrated machine learning classifier processes heart rate from a consumer-grade wearable. A simulator study (N=30) with two age groups (20-25, 65-70 years) was conducted to evaluate acceptance and performance of the system. Acceptance evaluation resulted in high acceptance in both age groups. Older participants showed higher attitudes and intentions towards using the system compared to younger participants. Overall detection accuracy of 82.72% was achieved. The proposed system offers new options for in-vehicle human-machine interfaces, especially for driver drowsiness detection in the lower levels of automated driving.

References

[1]
Daimler AG. 2020. ATTENTION ASSIST: Drowsiness-detection system warns drivers to prevent them falling asleep momentarily. https://media.daimler.com/marsMediaSite/en/instance/ko.xhtml?oid=9361586(retrieved July 11, 2021).
[2]
Volkswagen AG. 2020. Driver Alert System. https://www.volkswagen.co.uk/technology/car-safety/driver-alert-system(retrieved July 11, 2021).
[3]
Christer Ahlstrom, Carina Fors, Anna Anund, and David Hallvig. 2015. Video-based observer rated sleepiness versus self-reported subjective sleepiness in real road driving. Eur. Transp. Res. Rev. 7, 4 (23 November 2015), 38. https://doi.org/10.1007/s12544-015-0188-y
[4]
T. Akerstedt and M. Gillberg. 1990. Subjective and objective sleepiness in the active individual. International Journal of Neuroscience 52 (1990), 29–37. https://doi.org/10.3109/00207459008994241
[5]
Android. 2021. Android Automotive. https://source.android.com/devices/automotive (retrieved July 11, 2021).
[6]
Anna Anund, Carina Fors, David Hallvig, T. Åkerstedt, and G. Kecklund. 2013. Observer Rated Sleepiness and Real Road Driving: An Explorative Study. PLoS OnE 8, 5 (May 2013), 1–8. https://doi.org/10.1371/journal.pone.0064782
[7]
Paula Branco, Luís Torgo, and Rita P. Ribeiro. 2015. A Survey of Predictive Modelling under Imbalanced Distributions. CoRR (2015). https://doi.org/abs/1505.01658
[8]
Leo Breiman. 2001. Random forests. Machine Learning 45, 1 (oct 2001), 5–32. https://doi.org/10.1023/A:1010933404324
[9]
Minho Choi, Gyogwon Koo, Minseok Seo, and Sang Woo Kim. 2018. Wearable Device-Based System to Monitor a Driver’s Stress, Fatigue, and Drowsiness. IEEE Trans. Instrum. Meas. 67, 3 (March 2018), 634–645. https://doi.org/10.1109/TIM.2017.2779329
[10]
European Comission. 2014. Final Report Summary - HARKEN (Heart and respiration in-car embedded nonintrusive sensors) | Report Summary | HARKEN | FP7 | CORDIS | European Commission. https://cordis.europa.eu/project/rcn/103870/reporting/en(retrieved July 11, 2021).
[11]
Fred D. Davis. 1989. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly 13, 3 (1989), 319–340. http://www.jstor.org/stable/249008
[12]
M. Doudou, A. Bouabdallah, and V. Berge-Cherfaoui. 2019. Driver Drowsiness Measurement Technologies: Current Research, Market Solutions, and Challenges. Int. J. Intell. Transp. Syst. Res. (12 Sep 2019). https://doi.org/10.1007/s13177-019-00199-w
[13]
EuroNCAP. 2017. EuroNCAP 2025 Roadmap. Technical Report. 1–17 pages. https://cdn.euroncap.com/media/30700/euroncap-roadmap-2025-v4.pdf
[14]
Pia M. Forsman, Bryan J. Vila, Robert A. Short, Christopher G. Mott, and Hans P.A. Van Dongen. 2013. Efficient driver drowsiness detection at moderate levels of drowsiness. Accident Analysis & Prevention 50 (2013), 341 – 350. https://doi.org/10.1016/j.aap.2012.05.005
[15]
Fabian Friedrichs and Bin Yang. 2010. Drowsiness monitoring by steering and lane data based features under real driving conditions. In European Signal Processing Conference. 209–213. https://www.iss.uni-stuttgart.de/forschung/publikationen/friedrichs-eusipco2010.pdf
[16]
Anna-Katharina Frison, Laura Aigner, Philipp Wintersberger, and Andreas Riener. 2018. Who is Generation A?: Investigating the Experience of Automated Driving for Different Age Groups. In Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (Toronto, ON, Canada) (AutomotiveUI ’18). ACM, New York, NY, USA, 94–104. https://doi.org/10.1145/3239060.3239087
[17]
Rongrong Fu, Hong Wang, and Wenbo Zhao. 2016. Dynamic driver fatigue detection using hidden Markov model in real driving condition. Expert Systems with Applications 63 (2016), 397–411. https://doi.org/10.1016/j.eswa.2016.06.042
[18]
Konstantinos Georgiou, Andreas V. Larentzakis, Nehal N. Khamis, Ghadah I. Alsuhaibani, Yasser A. Alaska, and Elias J. Giallafos. 2018. Can Wearable Devices Accurately Measure Heart Rate Variability? A Systematic Review. Folia Med. 60, 1 (2018), 7 – 20. https://doi.org/10.2478/folmed-2018-0012
[19]
Jasper Gielen and Jean-Marie Aerts. 2019. Feature Extraction and Evaluation for Driver Drowsiness Detection Based on Thermoregulation. Applied Sciences 9, 17 (2019). https://doi.org/10.3390/app9173555
[20]
IPG Automotive GmbH. 2020. CarMaker: Virtual testing of automobiles and light-duty vehicles. https://ipg-automotive.com/products-services/simulation-software/carmaker/(retrieved July 11, 2021).
[21]
Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. 2009. The WEKA Data Mining Software: An Update. SIGKDD Explor. 11, 1 (2009), 10–18. https://doi.org/10.1145/1656274.1656278
[22]
Imali T. Hettiarachchi, Samer Hanoun, Darius Nahavandi, and Saeid Nahavandi. 2019. Validation of Polar OH1 optical heart rate sensor for moderate and high intensity physical activities. PLOS ONE 14, 5 (05 2019), 1–13. https://doi.org/10.1371/journal.pone.0217288
[23]
M. Ingre, T. Åkerstedt, B. Peters, A. Anund, G. Kecklund, and A. Pickles. 2006. Subjective sleepiness and accident risk avoiding the ecological fallacy. J. Sleep Res. 15, 2 (2006), 142–148. https://doi.org/10.1111/j.1365-2869.2006.00517.x
[24]
S. Jung, H. Shin, and W. Chung. 2014. Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel. IET Intell. Transp. Syst. 8, 1 (Feb 2014), 43–50. https://doi.org/10.1049/iet-its.2012.0032
[25]
Thomas Kundinger and Andreas Riener. 2020. The Potential of Wrist-Worn Wearables for Driver Drowsiness Detection: A Feasibility Analysis. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (Genoa, Italy) (UMAP’20). Association for Computing Machinery, New York, NY, USA, 117–125. https://doi.org/10.1145/3340631.3394852
[26]
Thomas Kundinger, Andreas Riener, and Nikoletta Sofra. 2017. A Robust Drowsiness Detection Method based on Vehicle and Driver Vital Data. In Mensch und Computer 2017 - Workshopband, Manuel Burghardt, Raphael Wimmer, Christian Wolff, and Christa Womser-Hacker (Eds.). Gesellschaft für Informatik e.V., Regensburg.
[27]
Thomas Kundinger, Andreas Riener, Nikoletta Sofra, and Klemens Weigl. 2018. Drowsiness Detection and Warning in Manual and Automated Driving: Results from Subjective Evaluation. In Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (Toronto, ON, Canada) (AutomotiveUI’18). 229–236. https://doi.org/10.1145/3239060.3239073
[28]
Thomas Kundinger, Andreas Riener, Nikoletta Sofra, and Klemens Weigl. 2020. Driver Drowsiness in Automated and Manual Driving: Insights from a Test Track Study. In Proceedings of the 25th International Conference on Intelligent User Interfaces(Cagliari, Italy) (IUI’20). Association for Computing Machinery, New York, NY, USA, 369–379. https://doi.org/10.1145/3377325.3377506
[29]
Thomas Kundinger, Nikoletta Sofra, and Andreas Riener. 2020. Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection. Sensors 20, 4 (2020). https://doi.org/10.3390/s20041029
[30]
Thomas Kundinger, Phani Krishna Yalavarthi, Andreas Riener, Philipp Wintersberger, and Clemens Schartmüller. 2020. Feasibility of smart wearables for driver drowsiness detection and its potential among different age groups. International Journal of Pervasive Computing and Communications (2020). https://doi.org/10.1108/IJPCC-03-2019-0017
[31]
B. Lee, B. Lee, and W. Chung. 2016. Standalone Wearable Driver Drowsiness Detection System in a Smartwatch. IEEE Sens. J. 16, 13 (July 2016), 5444–5451. https://doi.org/10.1109/JSEN.2016.2566667
[32]
Boon-leng Lee, Boon-giin Lee, G. Li, and W.-Y. Chung. 2014. Wearable Driver Drowsiness Detection System Based on Smartwatch. In Korea Institute of Signal Processing and Systems, Vol. 15. 134–146.
[33]
Hyeonjeong Lee, Jaewon Lee, and Miyoung Shin. 2019. Using Wearable ECG/PPG Sensors for Driver Drowsiness Detection Based on Distinguishable Pattern of Recurrence Plots. Electronics 8, 2 (2019). https://doi.org/10.3390/electronics8020192
[34]
L. B. Leng, L. B. Giin, and W. Chung. 2015. Wearable driver drowsiness detection system based on biomedical and motion sensors. IEEE Sens. J.https://doi.org/10.1109/ICSENS.2015.7370355
[35]
Lexus. 2018. Lexus Safety System+. https://drivers.lexus.com/lexus-drivers-theme/pdf/LSS+%20Quick%20Guide%20Link.pdf(retrieved July 11, 2021).
[36]
QhanZhe Li, Juan Wu, Shin-Dug Kim, and Cheong-Ghil Kim. 2014. Hybrid Driver Fatigue Detection System Based on Data Fusion with Wearable Sensor Devices.
[37]
Alina Mashko. 2017. Subjective Methods for the Assessment of Driver Drowsiness. Acta Polytechnica CTU Proceedings 12 (dec 2017), 64. https://doi.org/10.14311/app.2017.12.0064
[38]
Aqsa Mehreen, Syed Muhammad Anwar, Muhammad Haseeb, Muhammad Majid, and Muhammad Obaid Ullah. 2019. A Hybrid Scheme for Drowsiness Detection Using Wearable Sensors. IEEE Sensors Journal 19, 13 (2019), 5119–5126. https://doi.org/10.1109/JSEN.2019.2904222
[39]
Timothy H. Monk. 1989. A visual analogue scale technique to measure global vigor and affect. Psychiatry Res. 27, 1 (1989), 89 – 99. https://doi.org/10.1016/0165-1781(89)90013-9
[40]
Drew M. Morris, June J. Pilcher, and Fred S. Switzer III. 2015. Lane heading difference: An innovative model for drowsy driving detection using retrospective analysis around curves. Accident Analysis & Prevention 80 (2015), 117 – 124. https://doi.org/10.1016/j.aap.2015.04.007
[41]
Polar. 2020. Polar OH1 Optical Heart Rate Sensor. https://www.polar.com/us-en/products/accessories/oh1-optical-heart-rate-sensor(retrieved July 11, 2021).
[42]
Polar. 2020. Polar SDK. https://www.polar.com/en/developers/sdk (retrieved July 11, 2021).
[43]
Mohsen Poursadeghiyan, Adel Mazloumi, Gebraeil Nasl Saraji, Ali Niknezhad, Arash Akbarzadeh, and Mohammad Hossein Ebrahimi. 2017. Determination the Levels of Subjective and Observer Rating of Drowsiness and Their Associations with Facial Dynamic Changes. Iranian J. Public Health 46, 1 (2017), 93–102. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5401941/)
[44]
Herlina Rahim, Ahmad Dalimi, and Haliza Jaafar. 2015. Detecting Drowsy Driver Using Pulse Sensor. J. Teknol. 73 (03 2015). https://doi.org/10.11113/jt.v73.4238
[45]
M. V. Ramesh, A. K. Nair, and A. T. Kunnathu. 2011. Real-Time Automated Multiplexed Sensor System for Driver Drowsiness Detection. In 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing. 1–4. https://doi.org/10.1109/wicom.2011.6040613
[46]
Arun Sahayadhas, Kenneth Sundaraj, and Murugappan Murugappan. 2012. Detecting Driver Drowsiness Based on Sensors: A Review. Sensors (Basel) 12, 12 (2012), 16937–16953. https://doi.org/10.3390/s121216937
[47]
Azmeh Shahid, Kate Wilkinson, Shai Marcu, and Colin M. Shapiro. 2012. Stanford Sleepiness Scale (SSS). Springer New York, 369–370.
[48]
Society of Automotive Engineers (SAE) International. 2018. Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. https://doi.org/10.4271/J3016_202104
[49]
Jose Solaz, Jose Laparra-Hernandez, Daniel Bande, Noelia Rodriguez, Sergio Veleff, Jose Gerpe, and Enrique Medina. 2016. Drowsiness Detection Based on the Analysis of Breathing Rate Obtained from Real-time Image Recognition. Transportation Research Procedia 14 (2016), 3867 – 3876. https://doi.org/10.1016/j.trpro.2016.05.472 Transport Research Arena TRA2016.
[50]
U. Trutschel, B. Sirois, D. Sommer, M. Golz, and D. Edwards. 2011. PERCLOS: An Alertness Measure of the Past. In Driving Assessment 2011: 6th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design. 172–179. https://doi.org/10.17077/drivingassessment.1394
[51]
IDC Corporate USA. 2019. Worldwide Wearables Market to Top 300 Million Units in 2019 and Nearly 500 Million Units in 2023. https://www.idc.com/getdoc.jsp?containerId=prUS45737919(retrieved July 11, 2021).
[52]
Veronika Weinbeer, T. Muhr, Klaus Bengler, C. Baur, J. Radlmayr, and J. Bill. 2017. Highly automated driving: How to get the driver drowsy and how does drowsiness influence various take-over-aspects?. In 8. Tagung Fahrerassistenz. Lehrstuhl für Fahrzeugtechnik mit TÜV SÜD Akademie. https://mediatum.ub.tum.de/1421309
[53]
J. Wörle, B. Metz, C. Thiele, and G. Weller. 2019. Detecting sleep in drivers during highly automated driving: The potential of physiological parameters. IET Intell. Transp. Syst. 13, 8 (2019), 1241–1248. https://doi.org/10.1049/iet-its.2018.5529
[54]
W. Zhang, B. Cheng, and Y. Lin. 2012. Driver drowsiness recognition based on computer vision technology. Tsinghua Sci. Technol. 17, 3 (June 2012), 354–362. https://doi.org/10.1109/NER.2015.7146721

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  • (2024)Move, Connect, Interact: Introducing a Design Space for Cross-Traffic InteractionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785808:3(1-40)Online publication date: 9-Sep-2024
  • (2024)A Novel Hybrid Approach for Driver Drowsiness Detection Using a Custom Deep Learning ModelIEEE Access10.1109/ACCESS.2024.343861712(126866-126884)Online publication date: 2024
  • (2023)Drowsiness Mitigation Through Driver State Monitoring Systems: A Scoping ReviewHuman Factors: The Journal of the Human Factors and Ergonomics Society10.1177/00187208231208523Online publication date: 20-Nov-2023
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cover image ACM Conferences
AutomotiveUI '21: 13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
September 2021
306 pages
ISBN:9781450380638
DOI:10.1145/3409118
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 20 September 2021

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Author Tags

  1. acceptance
  2. automated driving
  3. driver drowsiness detection
  4. machine learning
  5. prototype
  6. simulator study
  7. wearable devices

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  • (2024)Move, Connect, Interact: Introducing a Design Space for Cross-Traffic InteractionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785808:3(1-40)Online publication date: 9-Sep-2024
  • (2024)A Novel Hybrid Approach for Driver Drowsiness Detection Using a Custom Deep Learning ModelIEEE Access10.1109/ACCESS.2024.343861712(126866-126884)Online publication date: 2024
  • (2023)Drowsiness Mitigation Through Driver State Monitoring Systems: A Scoping ReviewHuman Factors: The Journal of the Human Factors and Ergonomics Society10.1177/00187208231208523Online publication date: 20-Nov-2023
  • (2023)Design and Implementation of a Drowsiness Detection System Up to Extended Head Angle Using FaceMesh Machine Learning SolutionMachine Intelligence and Emerging Technologies10.1007/978-3-031-34622-4_7(79-90)Online publication date: 11-Jun-2023
  • (2021)MACHINE LEARNING BASED CLOUD MUSIC APPLICATION WITH FACIAL RECOGNITION USING ANDROID STUDIO (MUSYNC)American International Journal of Sciences and Engineering Research10.46545/aijser.v4i1.213(36-52)Online publication date: 27-Oct-2021

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