Machine Learning for Automobile Driver Identification Using Telematics Data

Abstract 14900: Human Atrial Fibrillation Drivers Revealed by Machine Learning Using Multicomponent Electrograms and 3D CE-MRI Structural Features

Circulation ◽

10.1161/circ.142.suppl_3.14900 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

Aleksei Mikhailov ◽

Brian Hansen ◽

Matthew Fazio ◽

Stanislav Zakharkin ◽

Jichao Zhao ◽

...

Keyword(s):

Machine Learning ◽

Atrial Fibrillation ◽

Gold Standard ◽

Near Infrared ◽

Structural Features ◽

Multiple Features ◽

Mri Features ◽

Magnetic Resonance Imaging Mri ◽

Driver Identification

Introduction: Conventional multielectrode mapping is not sufficient to reveal subsurface intramural activation. Thus, atrial fibrillation (AF) driver identification remains challenging. To overcome these limitations we utilized machine learning (ML) to identify AF drivers based on the combination of electrogram (EGM) and 3D structural magnetic resonance imaging (MRI) features. Hypothesis: Detailed electrogram features analysis, including minor deflections, combined with local structural features, can be used to define AF driver. Methods: Sustained AF was mapped in coronary perfused explanted human atria (n=7) with near-infrared optical mapping (NIOM) (0.3-0.9mm 2 resolution) and 64-electrode mapping catheter (3mm 2 resolution). Unipolar EGMs were analyzed for multiple features of the steepest negative deflection and the 2nd-4th steepest deflections in multicomponent EGMs. Atria underwent 9.4T MRI (154-180μm 3 resolution) with gadolinium enhancement and histology validation of fibrosis. Both 3D structural and EGM data from NIOM defined driver and non-driver regions were processed by ML algorithms (LR; PLSDA; GBM; CRF; PSVM; RSVM) using double cross-validation. Results: AF drivers’ reentrant tracks were defined by NIOM activation mapping, the gold-standard, and confirmed by targeted ablation. The best performing ML algorithm (PLSDA) correctly classified mapped driver region with 76.1% accuracy on the testing data. The most important features included sub-endocardial fibrosis, sub-epicardial fiber orientation, local wall thickness, beat-to-beat variability of multicomponent EGM deflections. Conclusions: The ML models pre-trained on combined EGM and structural features allow efficient classification of AF driver vs non-driver regions defined by the NIOM gold-standard. The results suggest that AF driver substrates formed by the combination of 3D fibrotic structural features, which correlate with local EGM characteristics.

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Discrete Wavelet Transform for Generative Adversarial Network to Identify Drivers Using Gyroscope and Accelerometer Sensors

10.36227/techrxiv.14794461 ◽

2021 ◽

Author(s):

Ruhallah Ahmadian ◽

Mehdi Ghatee ◽

Johan Wahlstrom

Keyword(s):

Machine Learning ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Data Augmentation ◽

Discrete Wavelet ◽

Learning Methods ◽

Generative Adversarial Network ◽

Machine Learning Methods ◽

Adversarial Network ◽

Driver Identification

Driver identification is an important research area in intelligent transportation systems, with applications in commercial freight transport and usage-based insurance. One way to perform the identification is to use smartphones as sensor devices. By extracting features from smartphone-embedded sensors, various machine learning methods can identify the driver. The identification becomes particularly challenging when the number of drivers increases. In this situation, there is often not enough data for successful driver identification. This paper uses a Generative Adversarial Network (GAN) for data augmentation to solve the problem of lacking data. Since GAN diversifies the drivers' data, it extends the applicability of the driver identification. Although GANs are commonly used in image processing for image augmentation, their use for driving signal augmentation is novel. Our experiments prove their utility in generating driving signals emanating from the Discrete Wavelet Transform (DWT) on smartphones' accelerometer and gyroscope signals. After collecting the augmented data, their histograms along the overlapped windows are fed to machine learning methods covered by a Stacked Generalization Method (SGM). The presented hybrid GAN-SGM approach identifies drivers with 97% accuracy, 98% precision, 97% recall, and 97% F1-measure that outperforms standard machine learning methods that process features extracted by the statistical, spectral, and temporal approaches.

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Who’s Driving My Car? A Machine Learning based Approach to Driver Identification

Proceedings of the 4th International Conference on Information Systems Security and Privacy ◽

10.5220/0006633403670372 ◽

2018 ◽

Cited By ~ 6

Author(s):

Fabio Martinelli ◽

Francesco Mercaldo ◽

Vittoria Nardone ◽

Albina Orlando ◽

Antonella Santone

Keyword(s):

Machine Learning ◽

Driver Identification

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Discrete Wavelet Transform for Generative Adversarial Network to Identify Drivers Using Gyroscope and Accelerometer Sensors

10.36227/techrxiv.14794461.v1 ◽

2021 ◽

Author(s):

Ruhallah Ahmadian ◽

Mehdi Ghatee ◽

Johan Wahlstrom

Keyword(s):

Machine Learning ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Data Augmentation ◽

Discrete Wavelet ◽

Learning Methods ◽

Generative Adversarial Network ◽

Machine Learning Methods ◽

Adversarial Network ◽

Driver Identification

Driver identification is an important research area in intelligent transportation systems, with applications in commercial freight transport and usage-based insurance. One way to perform the identification is to use smartphones as sensor devices. By extracting features from smartphone-embedded sensors, various machine learning methods can identify the driver. The identification becomes particularly challenging when the number of drivers increases. In this situation, there is often not enough data for successful driver identification. This paper uses a Generative Adversarial Network (GAN) for data augmentation to solve the problem of lacking data. Since GAN diversifies the drivers' data, it extends the applicability of the driver identification. Although GANs are commonly used in image processing for image augmentation, their use for driving signal augmentation is novel. Our experiments prove their utility in generating driving signals emanating from the Discrete Wavelet Transform (DWT) on smartphones' accelerometer and gyroscope signals. After collecting the augmented data, their histograms along the overlapped windows are fed to machine learning methods covered by a Stacked Generalization Method (SGM). The presented hybrid GAN-SGM approach identifies drivers with 97% accuracy, 98% precision, 97% recall, and 97% F1-measure that outperforms standard machine learning methods that process features extracted by the statistical, spectral, and temporal approaches.

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Driver identification in intelligent vehicle systems using machine learning algorithms

IET Intelligent Transport Systems ◽

10.1049/iet-its.2017.0254 ◽

2019 ◽

Vol 13 (1) ◽

pp. 40-47 ◽

Cited By ~ 4

Author(s):

Zhengping Li ◽

Kai Zhang ◽

Bokui Chen ◽

Yuhan Dong ◽

Lin Zhang

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Intelligent Vehicle ◽

Vehicle Systems ◽

Driver Identification

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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