Drowsiness estimation under driving environment by heart rate variability and/or breathing rate variability with logistic regression analysis

Igasaki, Tomohiko; Nagasawa, Kazuki; Murayama, Nobuki; Hu, Zhencheng

doi:10.1109/bmei.2015.7401498

Cited by 28 publications

(18 citation statements)

References 1 publication

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“…Figure 12. Confusion matrices for the tested scenario: drowsy driver (0) versus wakeful ones (1). Each of the confusion matrices report the performance of the proposed pipeline in discriminating drowsy/wakeful drivers for a selected subject from the collected dataset.…”

Section: Resultsmentioning

confidence: 99%

“…The possibility to detect an attention state of a driver may facilitate evaluation of his/her fitness to drive a vehicle, facilitating the prevention of road accidents. It is known that a correlation exists between drowsiness and heart rate variability (HRV), that is, a measure of heart activity over a beat-to-beat interval, so that estimating HRV of, for example, a driver, may permit obtaining useful information concerning possible 2 of 25 drowsiness [1]. The scientific studies of drowsiness allowed to understand that it is reflected in the activity of the central nervous system as well as of the autonomic nervous system (ANS).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, a simple method to estimate drowsiness is to monitor ANS activity. Specifically, heart rate variability (HRV), which recent studies confirmed having high signal-to-noise ratio in biological signals, besides the fact that its measurement is non-invasive, reflects ANS activity [1]. Many researchers have over time deepened the study and the correlation between HRV and the level of human attention.…”

Section: Introductionmentioning

confidence: 99%

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Ad-Hoc Shallow Neural Network to Learn Hyper Filtered PhotoPlethysmoGraphic (PPG) Signal for Efficient Car-Driver Drowsiness Monitoring

2019

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In next-generation cars, safety equipment related to assisted driving systems commonly known as ADAS (advanced driver-assistance systems) are of particular interest for the major car-makers. When we talk about the “ADAS system”, we mean the devices and sensors having the precise objective of improving and making car driving safer, and among which it is worth mentioning rain sensors, the twilight sensor, adaptive cruise control, automatic emergency braking, parking sensors, automatic signal recognition, and so on. All these devices and sensors are installed on the new homologated cars to minimize the risk of an accident and make life on board of the car easier. Some sensors evaluate the movement and the opening of the eyes, the position of the head and its angle, or some physiological signals of the driver obtainable from the palm of the hands placed in the steering. In the present contribution, the authors will present an innovative recognition and monitoring system of the driver’s attention level through the study of the photoplethysmographic (PPG) signal detectable from the palm of the driver’s hands through special devices housed in the steering of the car. Through a particular and innovative post-processing algorithm of the PPG signal through a hyper-filtering framework, then processed by a machine learning framework, the entire pipeline proposed will be able to recognize and monitor the attention level of the driver with high accuracy and acceptable timing.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ad-Hoc Shallow Neural Network to Learn Hyper Filtered PhotoPlethysmoGraphic (PPG) Signal for Efficient Car-Driver Drowsiness Monitoring

2019

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“…Drowsiness while driving can also be detected using a trajectory sensor [2,3] that monitors a vehicle's movement, as a driver's control becomes erratic if they are drowsy. Other researchers have proposed the use of biological data, such as electrocardiogram (ECG) [4][5][6], electrooculogram (EOG) [7][8][9], respiratory [10,11], electromyogram (EMG) [12] and electroencephalogram (EEG) [13][14][15][16] signals to assess drowsiness. In this study, we have selected EEG data as a parameter for assessing drowsiness, since it is fundamentally related to the activity of the human brain [17].…”

Section: Introductionmentioning

confidence: 99%

Drowsiness Estimation Using Electroencephalogram and Recurrent Support Vector Regression

Akbar

Igasaki

2019

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As a cause of accidents, drowsiness can cause economical and physical damage. A range of drowsiness estimation methods have been proposed in previous studies to aid accident prevention and address this problem. However, none of these methods are able to improve their estimation ability as the length of time or number of trials increases. Thus, in this study, we aim to find an effective drowsiness estimation method that is also able to improve its prediction ability as the subject’s activity increases. We used electroencephalogram (EEG) data to estimate drowsiness, and the Karolinska sleepiness scale (KSS) for drowsiness evaluation. Five parameters (α, β/α, (θ+α)/β, activity, and mobility) from the O1 electrode site were selected. By combining these parameters and KSS, we demonstrate that a typical support vector regression (SVR) algorithm can estimate drowsiness with a correlation coefficient (R2) of up to 0.64 and a root mean square error (RMSE) of up to 0.56. We propose a “recurrent SVR” (RSVR) method with improved estimation performance, as highlighted by an R2 value of up to 0.83, and an RMSE of up to 0.15. These results suggest that in addition to being able to estimate drowsiness based on EEG data, RSVR is able to improve its drowsiness estimation performance.

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“…These algorithms have been developed using data from sleep-deprived drivers, hence these are detecting SR fatigue. Another model, from Igasaki et al (2015), uses data of non-sleep-deprived drivers. Their logistic regression based on heart rate variability (HRV) measures and respiratory features yields 81% detection accuracy, however, it was only generated with data from eight male drivers.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Drivers’ States in Sleepiness Countermeasures Experiments Using Physiological and Eye Data – Hybrid Logistic and Linear Regression Model

Schmidt

Ochs

Decke

et al. 2017

Proceedings of the 9th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design: Dri

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Summary:Objective sleepiness evaluation is essential for the effect analysis of countermeasures for driver sleepiness, such as in-car stimulants. Furthermore, measuring drivers' sleepiness in simulator studies also becomes important when investigating causes for task-related sleepiness, for example driving on monotonous routes, which requires little driver engagement. To evaluate driver sleepiness and the effect of countermeasures, we developed a model for predicting sleepiness using both simple logistic and linear regression of heart rate variability, skin conductance and pupil diameter. The algorithm was trained and tested with data from 88 participants in driving simulator studies. A prediction accuracy of 77% was achieved and the model's sensitivity to thermal stimulation was shown.

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Drowsiness estimation under driving environment by heart rate variability and/or breathing rate variability with logistic regression analysis

Cited by 28 publications

References 1 publication

Ad-Hoc Shallow Neural Network to Learn Hyper Filtered PhotoPlethysmoGraphic (PPG) Signal for Efficient Car-Driver Drowsiness Monitoring

Ad-Hoc Shallow Neural Network to Learn Hyper Filtered PhotoPlethysmoGraphic (PPG) Signal for Efficient Car-Driver Drowsiness Monitoring

Drowsiness Estimation Using Electroencephalogram and Recurrent Support Vector Regression

Evaluating Drivers’ States in Sleepiness Countermeasures Experiments Using Physiological and Eye Data – Hybrid Logistic and Linear Regression Model

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