Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Detecting, Predicting, and Preventing Driver Drowsiness with Wrist-Wearable Devices

  • Conference paper
  • First Online:
Progress in Artificial Intelligence (EPIA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12981))

Included in the following conference series:

  • 2160 Accesses

Abstract

Insufficient sleep is a prominent problem in modern society with several negative effects and risks. One of the most serious consequences is traffic accidents caused by drowsy driving. Current solutions are focused on detecting drowsiness, where individuals need to reach a certain drowsiness level to receive an alarm, which may be too late to react. In this context, it is relevant to develop a wearable system that integrates the prediction of drowsiness and its prevention. By predicting the drowsy state, the driver can be warned in advance while still alert. To minimize further incidents, the reason why a state of drowsiness occurs must be identified, caused by a sleep disorder or sleep deprivation. The contribution of this work is to review the main scientific and commercial solutions, and perform automatic sleep staging based on heart rate variability. Results show that, although promising, this approach requires a larger dataset to consider a user-dependent scenario.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Collet, C., Musicant, O.: Associating vehicles automation with drivers functional state assessment systems: a challenge for road safety in the future. Front. Hum. Neurosci. 13, 131 (2019)

    Article  Google Scholar 

  2. Global status report on road safety 2018. World Health Organization (2018)

    Google Scholar 

  3. Gonçalves, M., et al.: Sleepiness at the wheel across Europe: a survey of 19 countries. J. Sleep Res. 24(3), 242–253 (2015)

    Google Scholar 

  4. Bioulac, S., et al.: Risk of motor vehicle accidents related to sleepiness at the wheel: a systematic review and meta-analysis. Sleep 40(10) (2017)

    Google Scholar 

  5. Thiffault, P., Bergeron, J.: Monotony of road environment and driver fatigue: a simulator study. Accid. Anal. Prev. 35(3), 381–391 (2003)

    Article  Google Scholar 

  6. Catarino, R., Spratley, J., Catarino, I., Lunet, N., Pais-Clemente, M.: Sleepiness and sleep-disordered breathing in truck drivers. Sleep Breathing 18(1), 59–68 (2014)

    Article  Google Scholar 

  7. Anne, T., John, M., Rohrbaugh, W., Hammer, M.C., Fuller, S.Z.: Factors associated with falling asleep at the wheel among long-distance truck drivers. Accid. Anal. Prev. 32(4), 493–504 (2000)

    Google Scholar 

  8. Greenfield, R., et al.: Truck drivers’ perceptions on wearable devices and health promotion: a qualitative study. BMC Public Health 16(1), 1–10 (2016)

    Google Scholar 

  9. Khan, M.Q., Lee, S.: A comprehensive survey of driving monitoring and assistance systems. Sensors 19(11), 2574 (2019)

    Article  Google Scholar 

  10. Doudou, M.., Bouabdallah, A.., Berge-Cherfaoui, V..: Driver drowsiness measurement technologies: current research, market solutions, and challenges. Int. J. Intell. Transp. Syst. Res. 18(2), 297–319 (2019). https://doi.org/10.1007/s13177-019-00199-w

    Article  Google Scholar 

  11. de Naurois, C.J., Bourdin, C., Stratulat, A., Diaz, E., Vercher, J.-L.: Detection and prediction of driver drowsiness using artificial neural network models. Accid. Anal. Prev.126, 95–104 (2019)

    Google Scholar 

  12. Kircher, A., Uddman, M., Sandin, J.: Vehicle control and drowsiness. Statens väg-och transportforskningsinstitut (2002)

    Google Scholar 

  13. Jaiswal, S.J., Owens, R.L., Malhotra, A.: Raising awareness about sleep disorders. Lung India Official Organ Indian Chest Soc. 34(3), 262 (2017)

    Google Scholar 

  14. Tobaldini, E., et al.: Sleep, sleep deprivation, autonomic nervous system and cardiovascular diseases. Neurosci. Biobehav. Rev. 74, 321–329 (2017)

    Article  Google Scholar 

  15. Senaratna, C.V., et al.: Prevalence of obstructive sleep apnea in the general population: a systematic review. Sleep Med. Rev. 34, 70–81 (2017)

    Google Scholar 

  16. Ameen, M.S., Cheung, L.M., Hauser, T., Hahn, M.A., Schabus, M.: About the accuracy and problems of consumer devices in the assessment of sleep. Sensors 19(19), 4160 (2019)

    Google Scholar 

  17. Sahayadhas, A., Sundaraj, K., Murugappan, M.: Detecting driver drowsiness based on sensors: a review. Sensors 12(12), 16937–16953 (2012)

    Article  Google Scholar 

  18. Kundinger, T., Sofra, N., Riener, A.: Assessment of the potential of wrist-worn wearable sensors for driver drowsiness detection. Sensors 20(4), 1029 (2020)

    Article  Google Scholar 

  19. Shahid, A., Wilkinson, K., Marcu, S., Shapiro, C.M.: Karolinska sleepiness scale (kss). In: STOP, THAT and One Hundred Other Sleep Scales, pp. 209–210. Springer (2011). https://doi.org/10.1007/978-1-4419-9893-4

  20. Awais, M., Badruddin, N., Drieberg, M.: A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability. Sensors 17(9), 1991 (2017)

    Article  Google Scholar 

  21. Consumer enthusiasm for wearable devices drives the market to 28.4% growth in 2020, according to idc (2021)

    Google Scholar 

  22. Lee, B.-L., Lee, B.-G., Chung, W.-Y.: Standalone wearable driver drowsiness detection system in a smartwatch. IEEE Sensors J. 16(13), 5444–5451 (2016)

    Article  Google Scholar 

  23. Choi, M., Koo, G., Seo, M., Kim, S.W.: Wearable device-based system to monitor a driver’s stress, fatigue, and drowsiness. IEEE Trans. Instrum. Meas. 67(3), 634–645 (2017)

    Google Scholar 

  24. Lee, H., Lee, J., Shin, M.: Using wearable ECG/PPG sensors for driver drowsiness detection based on distinguishable pattern of recurrence plots. Electronics 8(2), 192 (2019)

    Article  Google Scholar 

  25. Kundinger, T., Yalavarthi, P.K., Riener, A., Wintersberger, P., Schartmüller, C.: Feasibility of smart wearables for driver drowsiness detection and its potential among different age groups. Int. J. Pervasive Comput. Commun. 16, 1–23 (2020)

    Article  Google Scholar 

  26. Sztajzel, J., et al.: Heart rate variability: a noninvasive electrocardiographic method to measure the autonomic nervous system. Swiss Med. Wkly 134(35–36), 514–522 (2004)

    Google Scholar 

  27. Shaffer, F., Ginsberg, J.P.: An overview of heart rate variability metrics and norms. Front. Public Health 5, 258 (2017)

    Article  Google Scholar 

  28. Kamišalić, A., Fister, I., Turkanović, M., Karakatič, S.: Sensors and functionalities of non-invasive wrist-wearable devices: a review. Sensors 18(6), 1714 (2018)

    Article  Google Scholar 

  29. de Naurois, C.J., Bourdin, C., Bougard, C., Vercher, J.-L.: Adapting artificial neural networks to a specific driver enhances detection and prediction of drowsiness. Accid. Anal. Prev. 121, 118–128 (2018)

    Google Scholar 

  30. Liang, Y., et al.: Prediction of drowsiness events in night shift workers during morning driving. Accid. Anal. Prev. 126, 105–114 (2019)

    Google Scholar 

  31. Wang, J., Sun, S., Fang, S., Ting, F., Stipancic, J.: Predicting drowsy driving in real-time situations: using an advanced driving simulator, accelerated failure time model, and virtual location-based services. Accid. Anal. Prev. 99, 321–329 (2017)

    Article  Google Scholar 

  32. Radha, M., et al.: Sleep stage classification from heart-rate variability using long short-term memory neural networks. Sci. Rep. 9(1), 1–11 (2019)

    Google Scholar 

  33. Aboalayon, K.A.I., Faezipour, M., Almuhammadi, W.S., Moslehpour, S.: Sleep stage classification using EEG signal analysis: a comprehensive survey and new investigation. Entropy 18(9), 272 (2016)

    Google Scholar 

  34. De Zambotti, M., Cellini, N., Goldstone, A., Colrain, I.M., Baker, F.C.: Wearable sleep technology in clinical and research settings. Med. Sci. Sports Exerc. 51(7), 1538 (2019)

    Google Scholar 

  35. Ibáñez, V., Silva, J., Cauli, O.: A survey on sleep assessment methods. PeerJ 6, e4849 (2018)

    Article  Google Scholar 

  36. Walch, O., Huang, Y., Forger, D., Goldstein, C.: Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device. Sleep 42(12), zsz180 (2019)

    Google Scholar 

  37. Fonseca, P., et al.: Validation of photoplethysmography-based sleep staging compared with polysomnography in healthy middle-aged adults. Sleep 40(7), zsx097 (2017)

    Google Scholar 

  38. Beattie, Z., et al.: Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals. Physiol. Meas. 38(11), 1968 (2017)

    Google Scholar 

  39. Zhang, X., et al.: Sleep stage classification based on multi-level feature learning and recurrent neural networks via wearable device. Comput. Biol. Med. 103, 71–81 (2018)

    Google Scholar 

  40. Radha, M., Fonseca, P., Ross, M., Cerny, A., Anderer, P., Aarts, R.M.: LSTM knowledge transfer for HRV-based sleep staging. arXiv preprint arXiv:1809.06221 (2018)

  41. Molkkari, M., Tenhunen, M., Tarniceriu, A., Vehkaoja, A., Himanen, S.-L., Räsänen, E.: Non-linear heart rate variability measures in sleep stage analysis with photoplethysmography. In: 2019 Computing in Cardiology (CinC), p. 1. IEEE (2019)

    Google Scholar 

  42. Fedorin, I., Slyusarenko, K., Lee, W., Sakhnenko, N.: Sleep stages classification in a healthy people based on optical plethysmography and accelerometer signals via wearable devices. In: 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON), pp. 1201–1204. IEEE (2019)

    Google Scholar 

  43. Wei, Y., Qi, X., Wang, H., Liu, Z., Wang, G., Yan, X.: A multi-class automatic sleep staging method based on long short-term memory network using single-lead electrocardiogram signals. IEEE Access 7, 85959–85970 (2019)

    Article  Google Scholar 

  44. Rezaei, M., Mohammadi, H., Khazaie, H.: EEG/EOG/EMG data from a cross sectional study on psychophysiological insomnia and normal sleep subjects. Data in brief 15, 314–319 (2017)

    Article  Google Scholar 

  45. Gomes, P., Margaritoff, P., Silva, H.: pyHRV: development and evaluation of an open-source python toolbox for heart rate variability (HRV). In: Proceedings of International Conference on Electrical, Electronic and Computing Engineering (IcETRAN), pp. 822–828 (2019)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the European Regional Development Fund through the programme COMPETE by FCT (Portugal) in the scope of the project PEst-UID/CEC/00027/2015 and Sono ao Volante 2.0 - Information system for predicting sleeping while driving and detecting disorders or chronic sleep deprivation - NORTE-01-0247-FEDER-039720, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement. This research was partially supported by LIACC (FCT/UID/CEC/0027/2020).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Brígida Mónica Faria .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rodrigues, C., Faria, B.M., Reis, L.P. (2021). Detecting, Predicting, and Preventing Driver Drowsiness with Wrist-Wearable Devices. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science(), vol 12981. Springer, Cham. https://doi.org/10.1007/978-3-030-86230-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-86230-5_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86229-9

  • Online ISBN: 978-3-030-86230-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics