{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:39:13Z","timestamp":1773772753108,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2016,5,9]],"date-time":"2016-05-09T00:00:00Z","timestamp":1462752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Globally, 1.2 million people die and 50 million people are injured annually due to traffic accidents. These traffic accidents cost $500 billion dollars. Drunk drivers are found in 40% of the traffic crashes. Existing drunk driving detection (DDD) systems do not provide accurate detection and pre-warning concurrently. Electrocardiogram (ECG) is a proven biosignal that accurately and simultaneously reflects human\u2019s biological status. In this letter, a classifier for DDD based on ECG is investigated in an attempt to reduce traffic accidents caused by drunk drivers. At this point, it appears that there is no known research or literature found on ECG classifier for DDD. To identify drunk syndromes, the ECG signals from drunk drivers are studied and analyzed. As such, a precise ECG-based DDD (ECG-DDD) using a weighted kernel is developed. From the measurements, 10 key features of ECG signals were identified. To incorporate the important features, the feature vectors are weighted in the customization of kernel functions. Four commonly adopted kernel functions are studied. Results reveal that weighted feature vectors improve the accuracy by 11% compared to the computation using the prime kernel. Evaluation shows that ECG-DDD improved the accuracy by 8% to 18% compared to prevailing methods.<\/jats:p>","DOI":"10.3390\/s16050659","type":"journal-article","created":{"date-parts":[[2016,5,9]],"date-time":"2016-05-09T10:05:24Z","timestamp":1462788324000},"page":"659","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram"],"prefix":"10.3390","volume":"16","author":[{"given":"Chung","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China"}]},{"given":"Kim","family":"Tsang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China"}]},{"given":"Hao","family":"Chi","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China"}]},{"given":"Faan","family":"Hung","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,5,9]]},"reference":[{"key":"ref_1","unstructured":"(2004). World Report on Road Traffic Injury Prevention, World Health Organization."},{"key":"ref_2","first-page":"1442","article-title":"Economics of global burden of road traffic injuries and their relationship with health system variables","volume":"4","author":"Dalal","year":"2013","journal-title":"Int. J. Prev. Med."},{"key":"ref_3","unstructured":"(2007). Drinking and Driving: A Road Safety Manual for Decision-Makers and Practitioners, Global Road Safety Partnership."},{"key":"ref_4","unstructured":"(2013). Global Status Report on Road Safety 2013: Supporting A Decade of Action, World Health Organization, World Health Organization."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1078","DOI":"10.1109\/JSEN.2011.2163816","article-title":"Water-Cluster-Detecting Breath Sensor and Applications in Cars for Detecting Drunk or Drowsy Driving","volume":"12","author":"Sakairi","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.jsr.2015.06.007","article-title":"Drunk driving detection based on classification of multivariate time series","volume":"54","author":"Li","year":"2015","journal-title":"J. Saf. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dai, J., Teng, J., Bai, X., Shen, Z., and Xuan, D. (2010, January 22\u201325). Mobile phone based drunk driving detection. Proceedings of the 2010 IEEE 4th International Conference on-NO PERMISSIONS in Pervasive Computing Technologies for Healthcare (PervasiveHealth), Munich, Germany.","DOI":"10.4108\/ICST.PERVASIVEHEALTH2010.8901"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/TITB.2010.2091646","article-title":"Noninvasive Biological Sensor System for Detection of Drunk Driving","volume":"15","author":"Murata","year":"2011","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1932","DOI":"10.1109\/JBHI.2014.2305403","article-title":"An Innovative Nonintrusive Driver Assistance System for Vital Signal Monitoring","volume":"18","author":"Sun","year":"2014","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6832","DOI":"10.3390\/s130506832","article-title":"A Human ECG Identification System Based on Ensemble Empirical Mode Decomposition","volume":"13","author":"Zhao","year":"2013","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5627","DOI":"10.3390\/s150305627","article-title":"An SVM-Based Solution for Fault Detection in Wind Turbines","volume":"15","author":"Santos","year":"2015","journal-title":"Sensors"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"12784","DOI":"10.3390\/s140712784","article-title":"Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine","volume":"14","author":"Li","year":"2014","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"16965","DOI":"10.3390\/s131216965","article-title":"Automatic and Objective Assessment of Alternating Tapping Performance in Parkinson\u2019s Disease","volume":"13","author":"Memedi","year":"2013","journal-title":"Sensors"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1093\/alcalc\/agh178","article-title":"Episode-centred analysis of drinking to intoxication in university students","volume":"40","author":"Kypri","year":"2005","journal-title":"Alcohol Alcohol."},{"key":"ref_15","first-page":"289","article-title":"Acute alcohol intake and P-wave dispersion in healthy men","volume":"5","author":"Uyarel","year":"2005","journal-title":"Anatol. J. Cardiol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). Nature of Statistical Learning, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_17","unstructured":"Tompkins, W.J. (2000). Biomedical Digital Signal Processing C-Language Examples and Laboratory Experiments for the IBM\u00aePC, Prentice Hall."},{"key":"ref_18","unstructured":"Kyoso, M., and Uchiyama, A. (2001, January 25\u201328). Development of an ECG identification system. Proceedings of the IEEE 23rd Annual International Conference on Engineering in Medicine and Biology Society, Istanbul, Turkey."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"15179","DOI":"10.3390\/s150715179","article-title":"Recognition of a Phase-Sensitivity OTDR Sensing System Based on Morphologic Feature Extraction","volume":"15","author":"Sun","year":"2015","journal-title":"Sensors"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1002\/wics.149","article-title":"Support vector machine regularization","volume":"3","author":"Reeves","year":"2011","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hou, H., Gao, Y., and Liu, D. (2014, January 15\u201317). A support vector machine with maximal information coefficient weighted kernel functions for regression. Proceedings of the IEEE 2nd International Conference on Systems and Informatics (ICSAI), Shanghai, China.","DOI":"10.1109\/ICSAI.2014.7009420"},{"key":"ref_22","unstructured":"Zhu, W., Zeng, N., and Wang, N. (2010, January 14\u201317). Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS\u00ae implementations. Proceedings of the NESUG proceedings: Health Care and Life Sciences, Baltimore, MA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"16494","DOI":"10.3390\/s131216494","article-title":"Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier","volume":"13","author":"Li","year":"2013","journal-title":"Sensors"},{"key":"ref_24","unstructured":"MedGadget Toyota to Integrate ECG Sensors into Steering Wheels. Available online: http:\/\/www.medgadget.com\/2011\/07\/toyota-to-integrate-ecg-sensors-into-steering-wheels.html."},{"key":"ref_25","unstructured":"MedGadget Ford Unveils Contactless ECG Sensing Driver Seat. Available online: http:\/\/www.medgadget.com\/2011\/05\/ford-unveils-contactless-ecg-sensing-driver-seat.html."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/5\/659\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:23:33Z","timestamp":1760210613000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/5\/659"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,5,9]]},"references-count":25,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2016,5]]}},"alternative-id":["s16050659"],"URL":"https:\/\/doi.org\/10.3390\/s16050659","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,5,9]]}}}