Driver Identification and Verification From Smartphone Accelerometers Using Deep Neural Networks

Sánchez, Sara Hernández; Pozo, Rubén Fernández; Gómez, Luis A. Hernández

doi:10.1109/tits.2020.3008210

Cited by 27 publications

(17 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fig. 13 compares the proposed system with three baseline driver identification models based on inertial data, including Virojboonkiate et al [17], Sánchez et al [5], and Li et al [26] to study how the accuracy diminishes as the number of drivers increases. We have implemented these baseline models in 1 , according to their descriptions.…”

Section: F Performance Of Hybrid Model Of Gan and Sgmmentioning

confidence: 99%

“…• Supervised learning methods based on the user-defined features, such as Linear Discriminant Analysis [8], and Extreme Learning Machines [9]. • Deep networks based on the hidden features such as Gated Recurrent Unit (GRU) [5]. • Ensemble methods, such as gradient tree boosting [10], random forest [11], extra trees [12], and Stacked Generalization Method (SGM) [13].…”

mentioning

confidence: 99%

See 1 more Smart Citation

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

Ahmadian¹,

Ghatee²,

Wahlström³

2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: F Performance Of Hybrid Model Of Gan and Sgmmentioning

confidence: 99%

mentioning

confidence: 99%

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

Ahmadian¹,

Ghatee²,

Wahlström³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…However, these methods often violate the driver's privacy or enable the drivers to cheat. Nowadays, the modern driver identification systems collect data from in-vehicle sensors [3], GPS [4], inertial sensors [5], or their combination. The related literature have used the following techniques to classify the drivers:…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

2022

View full text Add to dashboard Cite

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% F1measure that outperforms standard machine learning methods that process features extracted by the statistical, spectral, and temporal approaches.

show abstract

Reliability of MEMS Accelerometers Embedded in Smart Mobile Devices for Robotics Applications

Bakirci

Cetin

2023

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Driver Identification and Verification From Smartphone Accelerometers Using Deep Neural Networks

Cited by 27 publications

References 24 publications

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

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

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

Reliability of MEMS Accelerometers Embedded in Smart Mobile Devices for Robotics Applications

Contact Info

Product

Resources

About