Variation of the Heartbeat and Activity as an Indicator of Drowsiness at the Wheel Using a Smartwatch
DOI:
https://doi.org/10.9781/ijimai.2015.3313Keywords:
Mobile Device, Wereable, Sensor, Smartwatch, SleepinessAbstract
Sleepiness is one of the first causal factors of accidents. An estimated 10-30% of road deaths are related to fatigue driving. A large number of research studies have been conducted to reduce the risk of accidents while driving. Many of these studies are based on the detection of biological signals by drowsiness/sleepiness. The activity of the autonomic nervous system (ANS) presented alterations during different physical states such as stress or sleepiness. This activity is measured by ECG (electroencephalogram) and, in different studies, it can be measured with the variation of the heart beat (HRV-Heart Rate Variability) in order to analyze it and then detect drowsiness/sleepiness in drivers. The main advantage is that HRV can be performed using non invasive and uncomfortable means compared to EEG sensors. New Wearables technologies are capable of measuring the heart beat and, further, using other sensors like Accelerometer and Gyroscope, embedded on a simple clock allow us to monitor the physical activity of the user. Our main goal is to use the pulsations measurements in conjunction with the physical activity for the detection of driver drowsiness/sleepiness in advance in order to prevent accidents derived from fatigue.Downloads
References
[1] Vicente, J., Laguna, P., Bartra, A., & Bailon, R. (2011). Detection of driver’s drowsiness by means of HRV analysis.2011 Computing in Cardiology (CinC), 89–92.
[2] Horne, J. a, & Reyner, L. a. (1995). Sleep related vehicle accidents. BMJ (Clinical Research Ed.), 310, 565–567. doi:10.1136/bmj.310.6991.1411b
[3] Epidemiological, A., & Study, M. (1998). Automobile Accidents in Patients with Sleep Apnea Syndrome.
[4] Leger, D. (1994). The cost of sleep-related accidents: a report for the National Commission on Sleep Disorders Research. Sleep, 17(2), 84–93.
[5] Horne, J., & Reyner, L. (1999). Vehicle accidents related to sleep: a review. Occupational and Environmental Medicine, 56, 289–294. doi:10.1136/oem.56.5.289
[6] Mahachandra, M., Yassierli, Sutalaksana, I. Z., & Suryadi, K. (2012). Sensitivity of heart rate variability as indicator of driver sleepiness.2012 Southeast Asian Network of Ergonomics Societies Conference: Ergonomics Innovations Leveraging User Experience and Sustainability, SEANES 2012, 0–5. doi:10.1109/SEANES.2012.6299577
[7] Elsenbruch, S., Harnish, M. J., & Orr, W. C. (1999). Heart rate variability during waking and sleep in healthy males and females. Sleep, 22(8), 1067–1071.
[8] Bonnet, M. H., & Arand, D. L. (1997). Heart rate variability: Sleep stage, time of night, and arousal influences. Electroencephalography and Clinical Neurophysiology, 102(96), 390–396. doi:10.1016/S0921-884X(96)96070-1
[9] Ayu, M. A., Ismail, S. A., Matin, A. F. A., & Mantoro, T. (2012). A Comparison Study of Classifier Algorithms for Mobile phone’s Accelerometer Based Activity Recognition. Procedia Engineering, 41(Iris), 224–229. doi:10.1016/j.proeng.2012.07.166
[10] Mortazavi, B. J., Pourhomayoun, M., Alsheikh, G., Alshurafa, N., Lee, S. I., & Sarrafzadeh, M. (2014). Determining the single best axis for exercise repetition recognition and counting on smart watches. Proceedings - 11th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2014, 33–38. doi:10.1109/BSN.2014.21.
[11] Bieber, G., Haescher, M., & Vahl, M. (2013).Sensor requirements for activity recognition on smart watches.Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments - PETRA ’13, 1–6. doi:10.1145/2504335.2504407
[12] Brezmes, T., Gorricho, J., & Cotrina, J. (2009). Activity Recognition from Accelerometer Data on a Mobile Phone.Test, 5518, 796–799. doi:10.1007/978-3-642-02481-8_120
[13] Lau, S. L., & David, K. (2010). Movement recognition using the accelerometer in smartphones. 2010 Future Network & Mobile Summit, 1–9. doi:http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5722356
[14] Casale, P., Pujol, O., & Radeva, P. (2011). Human activity recognition from accelerometer data using a wearable device.Pattern Recognition and Image Analysis, 289–296. Retrieved from http://link.springer.com/chapter/10.1007/978-3-642-21257-4_36
[15] Maguire, D., & Frisby, R. (2009). Comparison of Feature Classification Algorithms for Activity Recognition Based on Accelerometer and Heart Rate Data. 9th. IT & T Conference.
[16] Yang, J. Y., Wang, J. S., & Chen, Y. P. (2008). Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers. Pattern Recognition Letters, 29(16), 2213–2220. doi:10.1016/j.patrec.2008.08.002
[17] Fuentes, D., Gonzalez-Abril, L., Angulo, C., & Ortega, J. a. (2012). Online motion recognition using an accelerometer in a mobile device.Expert Systems with Applications, 39(3), 2461–2465. doi:10.1016/j.eswa.2011.08.098
[18] Fahim, M., Fatima, I., Lee, S., & Park, Y. T. (2013). EFM: Evolutionary fuzzy model for dynamic activities recognition using a smartphone accelerometer. Applied Intelligence, 39(3), 475–488. doi:10.1007/s10489-013-0427-7
[19] Hayata, Y., Bhuiyan, S., Kawanaka, H., & Oguri, K. (2013). Prediction of the Time When a Driver Reaches Critical Drowsiness Level Based on Driver Monitoring Before and While Driving, (Itsc), 6–11.
[20] Fernández MJL, Fernández JG, Rios-Aguilar S, BS Selvi, RG Crespo Control of attendance applied in higher education through mobile NFC technologies. Expert Systems with Applications 40 (11), 4478-4489
[21] JP Espada, VG Díaz, RG Crespo, OS Martínez, BCP G-Bustelo Using extended web technologies to develop Bluetooth multi-platform mobile applications for interact with smart things. Information Fusion 21, 30-41.
[22] Rios-Aguilar S., R González-Crespo, L de-la-Fuente-Valentin. Anonymous Assessment Information System for Higher Education Using Mobile Devices Advanced Learning Technologies (ICALT), 2014 IEEE 14th International.
[23] JP Espada, V Garcia Diaz, R Gonzalez Crespo, PG Bustelo, B Cristina. An intelligent Mobile Web Browser to adapt the mobile web as a function of the physical environment.Latin America Transactions, IEEE (Revista IEEE America Latina) 13 (2), 503-509.
Downloads
Published
-
Abstract52
-
PDF12
Submit Article
Information
