Detection and analysis: driver state with electrocardiogram (ECG)

Murugan, S.; Jayaraman, Selvaraj; Sahayadhas, Arun

doi:10.1007/s13246-020-00853-8

Cited by 74 publications

(38 citation statements)

References 24 publications

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“…Several studies have approached the relationship between driver sleepiness and HRV parameters by building sleepiness classifiers based on HRV features [19]- [26]. High sensitivity and specificity have been achieved by some of the laboratory studies that included subject dependent modeling [20], [24], [27]. Dropped performance has been observed by several studies when implementing subject independent modeling with subject-wise cross validation [24], [25], due to large individual variations in HRV.…”

Section: Introductionmentioning

confidence: 99%

Detecting Driver Sleepiness Using Consumer Wearable Devices in Manual and Partial Automated Real-Road Driving

Karlsson

Dahlman

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Driver sleepiness constitutes a well-known traffic safety risk. With the introduction of automated driving systems, the chance of getting sleepy and even falling asleep at wheel could increase further. Conventional sleepiness detection methods based on driving performance and behavior may not be available under automated driving. Methods based on physiological measurements such as heart rate variability (HRV) becomes a potential solution under automated driving. However, with reduced task load, HRV could potentially be affected by automated driving. Therefore, it is essential to investigate the influence of automated driving on the relation between HRV and sleepiness. Data from real-road driving experiments with 43 participants were used in this study. Each driver finished four trials with manual and partial automated driving under normal and sleep-deprived condition. Heart rate was monitored by consumer wearable chest bands. Subjective sleepiness based on Karolinska sleepiness scale was reported at five min interval as ground truth. Reduced heart rate and increased overall variability were found in association with severe sleepy episodes. A binary classifier based on the AdaBoost method was developed to classify alert and sleepy episodes. The results indicate that partial automated driving has small impact on the relationship between HRV and sleepiness. The classifier using HRV features reached area under curve (AUC) = 0.76 and it was improved to AUC = 0.88 when adding driving time and day/night information. The results show that Manuscript

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Section: Introductionmentioning

confidence: 99%

Detecting Driver Sleepiness Using Consumer Wearable Devices in Manual and Partial Automated Real-Road Driving

Karlsson

Dahlman

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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“…Wang et al reported that the measured values of BCG have no statistically significant differences compared with that of ECG in the time domain, frequency domain, and non-linear indicators in a calm state [21], which shows that BCG signal is a reliable method to extract heart rate. Therefore, BCG can be used as an accurate and effective non-disturbing detection method to evaluate the brain fatigue liking the ECG signal [22][23][24][25].…”

Section: Descriptions Charactersmentioning

confidence: 99%

“…Both of them can perform interference-free measurement and obtain human heart rate information without burden [16,20]. ECG signal has been widely used for brain fatigue detection [23][24][25][26], but lacks the application of a BCG signal in brain fatigue evaluation. In this study, a fiber-optic sensor embedded in a cushion was used to collect human BCG signals on a chair.…”

Section: Descriptions Charactersmentioning

confidence: 99%

A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram

Yang

et al. 2021

Healthcare

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Brain fatigue is often associated with inattention, mental retardation, prolonged reaction time, decreased work efficiency, increased error rate, and other problems. In addition to the accumulation of fatigue, brain fatigue has become one of the important factors that harm our mental health. Therefore, it is of great significance to explore the practical and accurate brain fatigue detection method, especially for quantitative brain fatigue evaluation. In this study, a biomedical signal of ballistocardiogram (BCG), which does not require direct contact with human body, was collected by optical fiber sensor cushion during the whole process of cognitive tasks for 20 subjects. The heart rate variability (HRV) was calculated based on BCG signal. Machine learning classification model was built based on random forest to quantify and recognize brain fatigue. The results showed that: Firstly, the heart rate obtained from BCG signal was consistent with the result displayed by the medical equipment, and the absolute difference was less than 3 beats/min, and the mean error is 1.30 ± 0.81 beats/min; secondly, the random forest classifier for brain fatigue evaluation based on HRV can effectively identify the state of brain fatigue, with an accuracy rate of 96.54%; finally, the correlation between HRV and the accuracy was analyzed, and the correlation coefficient was as high as 0.98, which indicates that the accuracy can be used as an indicator for quantitative brain fatigue evaluation during the whole task. The results suggested that the brain fatigue quantification evaluation method based on the optical fiber sensor cushion and machine learning can carry out real-time brain fatigue detection on the human brain without disturbance, reduce the risk of human accidents in human–machine interaction systems, and improve mental health among the office and driving personnel.

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“…In this case, the indicator obtained from the ECG recording, which is most often used in transport studies, is heart rate variability (HRV). This indicator reflects the adaptation of the organism to environmental conditions and external factors [12]. The calculation of this indicator is based on the analysis of R-R intervals of ECG recording (Fig.…”

Section: Traffic Conditions and Their Impact On The Reliability Of The Drivermentioning

confidence: 99%

Influence of mountain traffic conditions on the functional state of a bus driver

Postranskyy¹,

Boikiv²

2021

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Ensuring the transport process requires proper interaction of all parts of the system "driver - car - road - environment" and its subsystems. In this case, the driver is often a "weak" component of the system, and his actions can reduce the level of road users` safety. It should be noted that the reliability of the driver can be considered as the probability of his trouble-free and error-free operation, as well as the proper level of his regulatory mechanisms functioning. In this case, to analyze the activities and readiness of the driver for his professional activities, indicators of functional status are often used. Thus, the study of the "human factor" in the transport process is an important task to ensure the reliability of the whole transport system. Today the most of all transportation is carried out by road. The timeliness and safety of cargo delivery and passenger safety depend on the driver's actions. At the same time, the driver is influenced by a considerable number of external environmental factors during his work. One of the most important factors is the mountainous traffic conditions, which often have many changes in plan and the profile of roads. Another feature of such traffic conditions is the height above sea level, affecting the human body, particularly its functional state. Considering the above, the paper measures the heart rate variability of bus drivers moving on a route that was partly in the mountain's conditions. During the research, video recording and registration of the vehicle's geolocation were also carried out. This made it possible to establish indicators of the driver`s functional state in specific periods. After processing the obtained values, the influence of mountainous traffic conditions on the bus drivers` indicator of regulatory systems’ activity was established.

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Detection and analysis: driver state with electrocardiogram (ECG)

Cited by 74 publications

References 24 publications

Detecting Driver Sleepiness Using Consumer Wearable Devices in Manual and Partial Automated Real-Road Driving

Detecting Driver Sleepiness Using Consumer Wearable Devices in Manual and Partial Automated Real-Road Driving

A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram

Influence of mountain traffic conditions on the functional state of a bus driver

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