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

Pinto, João Ribeiro; Cardoso, Jaime S.; Lourenço, André; Carreiras, Carlos

doi:10.3390/s17102228

Cited by 96 publications

(68 citation statements)

References 60 publications

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“…Other related biometric NN architectures include the Radial Basis Function (RBF) NN [98,99] and Artificial NN (ANN) [100,101], Deep Belief Networks (DBN) [102,103], Convolutional Neural Networks (CNN) [104,105] and Recurrent Neural Networks (RNN) [106,107]. Finally, Table III presents some additional approaches mentioned in the pattern classification publications in biometric applications.…”

Section: Deep Learning For Biometricsmentioning

confidence: 99%

Pattern Mining Approaches Used in Sensor-Based Biometric Recognition: A Review

et al. 2019

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Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being-acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems.

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Section: Deep Learning For Biometricsmentioning

confidence: 99%

Pattern Mining Approaches Used in Sensor-Based Biometric Recognition: A Review

et al. 2019

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“…Recent publications on ECG monitoring deal with data processing and management (such as computational techniques, data analysis, compression techniques, etc.) [18][19][20][21], as well as new materials for ECG sensors [22,23] and miniaturization for portability [24]. Data processing of the Vitalmob system is implemented in a remote e-health platform, including an initial preprocessing and signal conditioning filtering, followed by a beat detection algorithm and a QRS-wave analysis.…”

Section: State Of the Artmentioning

confidence: 99%

A Quality and Ergonomic Heart Monitoring Device with User-Friendly App for Telemedicine

Bleda

Maestre

Corral

et al. 2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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During recent decades, the elderly population has grown rapidly, and the number of patients with chronic heart-related diseases has exploded. Many of them (e.g., congestive heart failure, some types of arrhythmia) require close medical supervision, imposing a major burden on healthcare resources in most western economies. Telemedicine is clearly emerging as a viable solution for health care to become sustainable by enabling medical monitoring at home without direct professional help, which would greatly reduce assistance costs. Specifically, continuous or frequent arterial blood pressure (ABP) and electrocardiogram (ECG) monitoring are important tools in the follow-up of many of these patients. A multipurpose (e.g. ABP and ECG) monitoring device that requires no medical assistance is highly desirable and would be of great value to patients. One such solution is presented and validated in this work. This paper presents the details of the proposed remote heart self-monitoring system, the testing procedure, and test results.

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

Cited by 96 publications

References 60 publications

Pattern Mining Approaches Used in Sensor-Based Biometric Recognition: A Review

Pattern Mining Approaches Used in Sensor-Based Biometric Recognition: A Review

A Quality and Ergonomic Heart Monitoring Device with User-Friendly App for Telemedicine

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

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