CardioWheel: ECG Biometrics on the Steering Wheel

Lourenço, André; Alves, Ana Priscila; Carreiras, Carlos; Duarte, Rodrigo; Fred, Ana

doi:10.1007/978-3-319-23461-8_27

Cited by 27 publications

(14 citation statements)

References 3 publications

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“…The initiative described in this paper aims to take a leap forward in off-the-person ECG biometrics in a driving environment, useful for automatic personalisation of settings, supervision of professional drivers in fleets, and modelling of driving patterns for detection of distraction moments and fatigue-related accidents [53,54]. It achieved this by overcoming unprecedented noise and signal loss hurdles, characteristic of signals acquired whilst driving, using electrodes embedded seamlessly in a steering wheel cover.…”

Section: Introductionmentioning

confidence: 99%

Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel

Pinto

Cardoso

Lourenço

et al. 2017

Sensors

Self Cite

102

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Electrocardiogram signals acquired through a steering wheel could be the key to seamless, highly comfortable, and continuous human recognition in driving settings. This paper focuses on the enhancement of the unprecedented lesser quality of such signals, through the combination of Savitzky-Golay and moving average filters, followed by outlier detection and removal based on normalised cross-correlation and clustering, which was able to render ensemble heartbeats of significantly higher quality. Discrete Cosine Transform (DCT) and Haar transform features were extracted and fed to decision methods based on Support Vector Machines (SVM), k-Nearest Neighbours (kNN), Multilayer Perceptrons (MLP), and Gaussian Mixture Models - Universal Background Models (GMM-UBM) classifiers, for both identification and authentication tasks. Additional techniques of user-tuned authentication and past score weighting were also studied. The method’s performance was comparable to some of the best recent state-of-the-art methods (94.9% identification rate (IDR) and 2.66% authentication equal error rate (EER)), despite lesser results with scarce train data (70.9% IDR and 11.8% EER). It was concluded that the method was suitable for biometric recognition with driving electrocardiogram signals, and could, with future developments, be used on a continuous system in seamless and highly noisy settings.

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Section: Introductionmentioning

confidence: 99%

Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel

Pinto

Cardoso

Lourenço

et al. 2017

Sensors

Self Cite

102

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“…In-vehicle sensors have successfully monitored drivers' heart rates for detecting drowsiness [75], using electrically conductive fabric wrapped around the steering wheel. ECG electrodes on the steering wheel have also been studied for driver identification from biometric signatures [76,77]. Heart rate and respiratory rate sensors were embedded in vehicle seats based on piezoresistive textiles [78].…”

Section: Sensor Technologies For Affect Detection Based On Physiologimentioning

confidence: 99%

A Review on Measuring Affect with Practical Sensors to Monitor Driver Behavior

Welch

Harnett

Lee

2019

Safety

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Using sensors to monitor signals produced by drivers is a way to help better understand how emotions contribute to unsafe driving habits. The need for intuitive machines that can interpret intentional and unintentional signals is imperative for our modern world. However, in complex human-machine work environments, many sensors will not work due to compatibility issues, noise, or practical constraints. This review focuses on practical sensors that have the potential to provide reliable monitoring and meaningful feedback to vehicle operators-such as drivers, train operators, pilots, astronauts-as well as being feasible for implementation and integration with existing work infrastructure. Such an affect-sensitive intelligent vehicle might sound an alarm if signals indicate the driver has become angry or stressed, take control of the vehicle if needed, and collaborate with other vehicles to build a stress map that improves roadway safety. Toward such vehicles, this paper provides a review of emerging sensor technologies for driver monitoring. In our research, we look at sensors used in affect detection. This insight is especially helpful for anyone challenged with accurately understanding affective information, like the autistic population. This paper also includes material on sensors and feedback for drivers from populations that may have special needs.Safety 2019, 5, 72 2 of 18 would be challenging to apply in vehicles because of noise sensitivity and data-processing requirements. Beyond engineering considerations, it would be unrealistic to expect daily drivers to apply the EEG sensor before each trip because it would be a new behavior and a time-consuming departure from the driver's routine in a world where it is already difficult to get people to wear seatbelts. Wearable sensing devices such as watches and eyeglasses that fit into a driver's established routine are more practical. Video cameras, reviewed in 2016 by Fernández et al. [1], are practical in the sense that users do not need to activate or touch them, but cameras and microphones also introduce privacy concerns and produce high bandwidth data that requires processing. New soft and textile-embedded sensor formats are promising because they can be fitted to vehicle interiors, and because they can detect safety-relevant activity using body contact data that is not as personally identifiable as video and audio streams. Figure 1 illustrates a pressure sensor for tracking a driver's grip pressure in (a) a body-worn format, and (b) a vehicle surface format. Wearable sensors such as the glove in (a) are more practical for daily use than, for example, blood sampling to measure glucose [2] or cortisol levels, or neural implants to detect brain activity in animal studies. Such invasive sampling can validate conclusions drawn from proxy signals available at the body surface, but a sensor glove is better for daily wear from the user's viewpoint. However, for this grip-tracking application, the driver would have to modify their behavior to put gloves on, would need ...

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“…Biometric technologies, such as fingerprint readers, can be used to prevent thefts. A promising research trend consists of using portable devices with biometric capabilities to capture ECG signals, which can then be used to detect important factors that affect safe driving behavior, such as distractions, drowsiness, and drunkenness [33]. Fig.…”

Section: Private Infrastructuresmentioning

confidence: 99%

Advanced Biometric Technologies: Emerging Scenarios and Research Trends

Genovese

Muñoz

Piuri

et al. 2018

Lecture Notes in Computer Science

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Biometric systems are the ensemble of devices, procedures, and algorithms for the automatic recognition of individuals by means of their physiological or behavioral characteristics. Although biometric systems are traditionally used in high-security applications, recent advancements are enabling the application of these systems in less-constrained conditions with non-ideal samples and with real-time performance. Consequently, biometric technologies are being increasingly used in a wide variety of emerging application scenarios, including public infrastructures, e-government, humanitarian services, and user-centric applications. This chapter introduces recent biometric technologies, reviews emerging scenarios for biometric recognition, and discusses research trends.

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CardioWheel: ECG Biometrics on the Steering Wheel

Cited by 27 publications

References 3 publications

Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel

Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel

A Review on Measuring Affect with Practical Sensors to Monitor Driver Behavior

Advanced Biometric Technologies: Emerging Scenarios and Research Trends

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