Advanced Motion-Tracking System with Multi-Layers Deep Learning Framework for Innovative Car-Driver Drowsiness Monitoring

Trenta, Francesca; Conoci, Sabrina; Rundo, Francesco; Battiato, Sebastiano

doi:10.1109/fg.2019.8756566

Cited by 36 publications

(36 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is interesting to highlight that the author has already patented and published a study in which, through an innovative low frame-rate motion magnification technique applied to the driver's face (captured by a vision camera with which the latest generation cars are being equipped), it is possible to reconstruct some features of the corresponding car-driver PPG signal in order to address the problem that could occur if the driver does not put a hand on any of the sensors located on the steering. More details are available in [21]. Specifically, the two analyzed methods (sampling of the PPG signal in the car steering versus reconstruction of the PPG signal by the motion magnification technique described in [21]) allow, in a complementary way, to always have a car-driver PPG signal that can be used by the car's control unit to correctly monitor the identity of the driver.…”

Section: Discussionmentioning

confidence: 99%

“…More details are available in [21]. Specifically, the two analyzed methods (sampling of the PPG signal in the car steering versus reconstruction of the PPG signal by the motion magnification technique described in [21]) allow, in a complementary way, to always have a car-driver PPG signal that can be used by the car's control unit to correctly monitor the identity of the driver. Therefore, in the absence of a reliable PPG signal from the steering or from other parts where the PPG sensing devices are installed (start button and car gear lever), the car's control unit would integrate the PPG signal reconstructed from the camera located on the base of the steering wheel, in order to apply the proposed identity recognition approach.…”

Section: Discussionmentioning

confidence: 99%

“…..n; x = (HP, LP); y = (HP, LP) ∀ n, m ∈ N + (14) Figure 8 shows an example of the DTW processing through the SKL metric between a signal patterns of the car-driver PPG signal (coming from biosensing device on the steering) and the corresponding signal pattern generated from the reference RD-M: Figure 8 shows an example of the DTW processing through the SKL metric between a signal patterns of the car-driver PPG signal (coming from biosensing device on the steering) and the corresponding signal pattern generated from the reference RD-M: The collected setup of similarity values ( ) are used as input for the deep long short-term memory (LSTM) model [20][21][22][23][24] described in the next subsection.…”

Section: The Dtw Processing Blockmentioning

confidence: 99%

“…The proposed deep learning pipeline is depicted in Figure 9. The collected setup of similarity values δ k xy (k) are used as input for the deep long short-term memory (LSTM) model [20][21][22][23][24] described in the next subsection.…”

Section: The Deep Lstm Processing Blockmentioning

confidence: 99%

See 3 more Smart Citations

Deep LSTM with Dynamic Time Warping Processing Framework: A Novel Advanced Algorithm with Biosensor System for an Efficient Car-Driver Recognition

Rundo

2020

Electronics

Self Cite

View full text Add to dashboard Cite

The latest generation of cars are increasingly equipped with driver assistance systems called ADAS (advanced driver assistance systems) which are able to assist the car driver in different driving scenarios, and in the most advanced automation levels, able to take over driving the car if required due to dangerous situations. Therefore, it is essential to adapt the ADAS specifically to the car-driver’s identity in order to better customize the driving assistance. To this end, algorithms that allow correct recognition of the vehicle driver are fundamental and preparatory. In this context, an algorithm for car-driver identity recognition is proposed which allows, with an accuracy close to 99%, recognition of the driver by means of a properly designed pipeline based on the analysis of the car driver PhotoPlethysmoGraphic (PPG) signal. The proposed approach makes use of deep long short-term memory (LSTM) architecture for learning such PPG signal features needed to discriminate one car driver from another. The extended validation and testing of the proposed system confirm the reliability of the proposed pipeline.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: The Dtw Processing Blockmentioning

confidence: 99%

Section: The Deep Lstm Processing Blockmentioning

confidence: 99%

See 2 more Smart Citations

Deep LSTM with Dynamic Time Warping Processing Framework: A Novel Advanced Algorithm with Biosensor System for an Efficient Car-Driver Recognition

Rundo

2020

Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Other activities, such as driving, was tackled in the literature in [189][190][191][192][193][194][195]. These studies proposed ECG monitoring for the purpose of observing drivers' health and emotions, with the ultimate goal of enhancing transport safety on the roads.…”

Section: Service-based Monitoring Systemsmentioning

confidence: 99%

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Serhani

Kassabi

Ismail

et al. 2020

Sensors

214

View full text Add to dashboard Cite

Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

show abstract

Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification

Rundo

Trenta

Leotta

et al. 2021

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

The latest generation cars are often equipped with advanced driver assistance systems usually known as ADAS (Advanced Driver Assistance Systems). These systems are able to assist the car driver leveraging several levels of automation. It is therefore essential to adapt the ADAS technology to the car driver's identity in order to personalize the provided assistance services. For these reasons, such car driver profiling algorithms have been developed by scientific community. The algorithm herein proposed is able to recognize the driver's identity with an accuracy close to 99% thanks to ad'hoc specific analysis of the driver's PhotoPlethysmoGraphic (PPG) signal. In order to rightly identify the driver profile, the proposed approach uses a 1D Dilated Temporal Convolutional Neural Network architecture to learn the features of the collected driver's PPG signal. The proposed deep architecture is able to correlate the specific PPG features with subject identity enabling the car ADAS services associated to the recognized identity. Extensive validation and testing of the developed pipeline confirmed its reliability and effectiveness.

show abstract

Advanced Motion-Tracking System with Multi-Layers Deep Learning Framework for Innovative Car-Driver Drowsiness Monitoring

Cited by 36 publications

References 14 publications

Deep LSTM with Dynamic Time Warping Processing Framework: A Novel Advanced Algorithm with Biosensor System for an Efficient Car-Driver Recognition

Deep LSTM with Dynamic Time Warping Processing Framework: A Novel Advanced Algorithm with Biosensor System for an Efficient Car-Driver Recognition

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification

Contact Info

Product

Resources

About