Drowsiness detection using heart rate variability analysis based on microcontroller unit

Hendra, Muhammad; Kurniawan, Danang; Chrismiantari, Riza Vina; Utomo, Trio Pambudi; Nuryani, Nuryani

doi:10.1088/1742-6596/1153/1/012047

Cited by 15 publications

(8 citation statements)

References 6 publications

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“…We chose ECG over EEG as a physiological measurement method because it requires fewer electrodes to be attached to the driver, making it easier to use. In addition, several driving fatigue detection studies have been conducted [10,[27][28][29][30][32][33][34][40][41][42] using heart rate-related sensors such as ECG, photoplethysmography, blood volume pulse, and oximeters, which showed that heart rate variability has a relationship with the driver's fatigue status. Therefore, ECG is a suitable choice for driving fatigue detection.…”

Section: Related Workmentioning

confidence: 99%

A New ECG Data Processing Approach to Developing an Accurate Driving Fatigue Detection Framework with Heart Rate Variability Analysis and Ensemble Learning

et al. 2023

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More than 1.3 million people are killed in traffic accidents annually. Road traffic accidents are mostly caused by human error. Therefore, an accurate driving fatigue detection system is required for drivers. Most driving fatigue detection studies concentrated on improving feature engineering and classification methods. We propose a novel driving fatigue detection framework concentrating on the development of the preprocessing, feature extraction, and classification stages to improve the classification accuracy of fatigue states. The proposed driving fatigue detection framework measures fatigue using a two-electrode ECG. The resampling method and heart rate variability analysis were used to extract features from the ECG data, and an ensemble learning model was utilized to classify fatigue states. To achieve the best model performance, 40 possible scenarios were applied: a combination of 5 resampling scenarios, 2 feature extraction scenarios, and 4 classification model scenarios. It was discovered that the combination of a resampling method with a window duration of 300 s and an overlap of 270 s, 54 extracted features, and AdaBoost yielded an optimum accuracy of 98.82% for the training dataset and 81.82% for the testing dataset. Furthermore, the preprocessing resampling method had the greatest impact on the model’s performance; it is a new approach presented in this study.

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Section: Related Workmentioning

confidence: 99%

A New ECG Data Processing Approach to Developing an Accurate Driving Fatigue Detection Framework with Heart Rate Variability Analysis and Ensemble Learning

et al. 2023

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“…All included studies had an observational, nonrandomized design [15,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. Data regarding participants enrolled in each study, investigated HRV parameters, clinical setting, and major findings were reported in Table 1 (Ref.…”

Section: Resultsmentioning

confidence: 99%

“…Data regarding participants enrolled in each study, investigated HRV parameters, clinical setting, and major findings were reported in Table 1 (Ref. [15,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]). The most of studies investigated the value of HRV measurements for sleepiness or drowsiness detection in drivers [15, 23-27, 29-31, 33, 34, 37, 39, 40], followed by stress [28,35,38] and fatigue [32,36] detection.…”

Section: Resultsmentioning

confidence: 99%

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Accurate and early detection of sleepiness, fatigue and stress levels in drivers through Heart Rate Variability parameters: a systematic review

Burlacu

Brinza

Brezulianu

et al. 2021

Rev. Cardiovasc. Med.

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Sleepiness, fatigue, and stress in drivers are the leading causes of car crashes. In the late two decades, there is an endeavor to monitor vital signs, stress levels, and fatigue using adapted sensors supported by technological advances. To the best of our knowledge, this systematic review is the first to investigate the role of HRV measurement for sleepiness, fatigue, and stress level monitoring in car drivers. A search was performed in PubMed, Embase, and Cochrane databases using prespecified keywords. Studies were considered for inclusion if they reported original data regarding the association between different HRV measurements and drivers' sleepiness, fatigue, or stress levels. Of the retrieved 749 citations, 19 studies were finally included. The sensibility and specificity of HRV significantly varied across studies, respectively 47.1%-95% and 74.6%-98%. Accuracy was also different, ranging from 56.6% to 95%. Nevertheless, in real-world conditions, confounding factors could affect sympathovagal tone and HRV. Multiple HRV parameters measurement rather than one parameter approach seems to be the optimal strategy for evaluating the vigilance state in drivers that it would be possible to achieve a good performance. As all studies were observational, data should be confirmed in randomized controlled trials. In conclusion, HRV represents a potentially valuable marker for sleepiness, fatigue, and stress monitoring in car drivers. HRV measurements could be implemented in future clinical models and sensors to detect early sleepiness and fatigue and prevent car crashes. More studies with larger populations are needed to support this evidence.

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“…Previous approaches to determine drowsiness have used computer vision, sensors or complex strategies using artificial intelligence for drowsiness detection and classification process while driving [16][17][18][19][20][21][22]. The aim of this paper is to use a smartphone as a practical method of determining drowsiness before and while driving.…”

Section: Introductionmentioning

confidence: 99%

Drowsiness Detection using Android Application and Mobile Vision Face API

Rajkumarsingh

Totah

2021

rdj

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Absence of forbearance among drivers, fatigue and irresponsible behaviour among drivers result in countless fatal crashes and road traffic injuries. Driver drowsiness is a highly problematic issue which impairs judgment and decision making among drivers resulting in fatal motor crashes. This paper describes a simple drowsiness detection approach for a smartphone with Android application using Android Studio 3.6.1 and Mobile Vision API for drowsiness detection before and while driving. Physiological analysis and a quick facial analysis were performed to check drowsiness before the driver starts driving. The smartphone camera was used for analysing the heart rate by tracking colour changes due to blood flow on the fingertip. Facial analysis was undertaken by Google Vision API which determined the head position, blinking duration and yawning frequency through the eye opening and mouth opening probabilities. The heart rate, blinking duration, yawning frequency and speeding were used as indicators for drowsiness. The facial analysis was repeated with speeding data while driving with results analysed each one minute. A performance accuracy of the combined results with speeding detection proved to be around 93.3%. Additional keywords: Drowsiness detection; Facial analysis; Heartrate; Mobile Vision API; Physiological analysis.

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Drowsiness detection using heart rate variability analysis based on microcontroller unit

Cited by 15 publications

References 6 publications

A New ECG Data Processing Approach to Developing an Accurate Driving Fatigue Detection Framework with Heart Rate Variability Analysis and Ensemble Learning

A New ECG Data Processing Approach to Developing an Accurate Driving Fatigue Detection Framework with Heart Rate Variability Analysis and Ensemble Learning

Accurate and early detection of sleepiness, fatigue and stress levels in drivers through Heart Rate Variability parameters: a systematic review

Drowsiness Detection using Android Application and Mobile Vision Face API

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