Detecting Distracted Driving Using a Wrist-Worn Wearable

Goel, Bharti; Dey, Arup Kanti; Bharti, Pratool; Ahmed, Kaoutar Ben; Chellappan, Sriram

doi:10.1109/percomw.2018.8480282

Cited by 17 publications

(6 citation statements)

References 8 publications

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“…These methods can be classical, deep or a combination of both classical and DL [29]. Goel et al [10] evaluated random forest, Naïve Bayes, SVM and decision tree for the detection of driving distraction. Random forest outperformed all the other strategies.…”

Section: B Distraction Detection Methodsmentioning

confidence: 99%

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Machine Learning and End-to-End Deep Learning for Monitoring Driver Distractions From Physiological and Visual Signals

et al. 2020

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It is only a matter of time until autonomous vehicles become ubiquitous; however, human driving supervision will remain a necessity for decades. To assess the driver's ability to take control over the vehicle in critical scenarios, driver distractions can be monitored using wearable sensors or sensors that are embedded in the vehicle, such as video cameras. The types of driving distractions that can be sensed with various sensors is an open research question that this study attempts to answer. This study compared data from physiological sensors (palm electrodermal activity (pEDA), heart rate and breathing rate) and visual sensors (eye tracking, pupil diameter, nasal EDA (nEDA), emotional activation and facial action units (AUs)) for the detection of four types of distractions. The dataset was collected in a previous driving simulation study. The statistical tests showed that the most informative feature/modality for detecting driver distraction depends on the type of distraction, with emotional activation and AUs being the most promising. The experimental comparison of seven classical machine learning (ML) and seven end-to-end deep learning (DL) methods, which were evaluated on a separate test set of 10 subjects, showed that when classifying windows into distracted or not distracted, the highest F1-score of 79% was realized by the extreme gradient boosting (XGB) classifier using 60-second windows of AUs as input. When classifying complete driving sessions, XGB's F1-score was 94%. The best-performing DL model was a spectro-temporal ResNet, which realized an F1-score of 75% when classifying segments and an F1-score of 87% when classifying complete driving sessions. Finally, this study identified and discussed problems, such as label jitter, scenario overfitting and unsatisfactory generalization performance, that may adversely affect related ML approaches.

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Section: B Distraction Detection Methodsmentioning

confidence: 99%

“…Thus, the relationship among the distractions, the driver reaction and, consequently, traffic accidents is complex. The distractions can occur in visual, manual or cognitive ways [9], [10]. In this study, cognitive, emotional, sensorimotor and mixed distractions are analyzed.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning and End-to-End Deep Learning for Monitoring Driver Distractions From Physiological and Visual Signals

et al. 2020

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“…By using a driving simulator with 28 participants, they were able to achieve an accuracy level of 98.43 % for detecting each state. [27] used a wearable smartwatch to detect instances of driver's distraction in a driving simulator. In their study, by collecting physiological data from 16 participants and utilizing multiple machine learning algorithms such as decision trees and support vector machines to classify different driving states, they achieved an average accuracy of 89 % in distraction detection.…”

Section: Background Studymentioning

confidence: 99%

Driver State and Behavior Detection Through Smart Wearables

Tavakoli¹,

Kumar²,

Boukhechba³

et al. 2021

Preprint

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Integrating driver, in-cabin, and outside environment's contextual cues into the vehicle's decision making is the centerpiece of semi-automated vehicle safety. Multiple systems have been developed for providing context to the vehicle, which often rely on video streams capturing drivers' physical and environmental states. While video streams are a rich source of information, their ability in providing context can be challenging in certain situations, such as low illuminance environments (e.g., night driving), and they are highly privacy-intrusive. In this study, we leverage passive sensing through smartwatches for classifying elements of driving context. Specifically, through using the data collected from 15 participants in a naturalistic driving study, and by using multiple machine learning algorithms such as random forest, we classify driver's activities (e.g., using phone and eating), outside events (e.g., passing intersection and changing lane), and outside road attributes (e.g., driving in a city versus a highway) with an average F1 score of 94.55, 98.27, and 97.86 % respectively, through 10-fold cross-validation. Our results show the applicability of multimodal data retrieved through smart wearable devices in providing context in real-world driving scenarios and pave the way for a better shared autonomy and privacy-aware driving data-collection, analysis, and feedback for future autonomous vehicles.

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“…Currently, Apple and Android watch users can precisely track various activities, including stand, walk, run and other physical exercises throughout the day. In research, smartwatch-based HAR system have been explored for elderly assistance [11], detection of self-harming activities in psychiatric facilities [12], distracted driving [13], smoking activities [14], speed detection [15] and more. In this section, we have discussed only recent works related to face touching activities in the context of Covid-19 pandemic.…”

Section: Related Workmentioning

confidence: 99%

CovidAlert -- A Wristwatch-based System to Alert Users from Face Touching

Roy,

Seethi,

Lake

et al. 2021

Preprint

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Worldwide 219 million people have been infected and 4.5 million have lost their lives in ongoing Covid-19 pandemic. Until vaccines became widely available, precautions and safety measures like wearing masks, physical distancing, avoiding face touching were some of the primary means to curb the spread of virus. Face touching is a compulsive human behavior that can not be prevented without constantly making a conscious effort, even then it is inevitable. To address this problem, we have designed a smartwatch-based solution, CovidAlert, that leverages Random Forest algorithm trained on accelerometer and gyroscope data from the smartwatch to detect hand transition to face and sends a quick haptic alert to the users. CovidAlert is highly energy efficient as it employs STA/LTA algorithm as a gatekeeper to curtail the usage of Random Forest model on the watch when user is inactive. The overall accuracy of system is 88.4% with low false negatives and false positives. We also demonstrated the system viability by implementing it on a commercial Fossil Gen 5 smartwatch.

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Detecting Distracted Driving Using a Wrist-Worn Wearable

Cited by 17 publications

References 8 publications

Machine Learning and End-to-End Deep Learning for Monitoring Driver Distractions From Physiological and Visual Signals

Machine Learning and End-to-End Deep Learning for Monitoring Driver Distractions From Physiological and Visual Signals

Driver State and Behavior Detection Through Smart Wearables

CovidAlert -- A Wristwatch-based System to Alert Users from Face Touching

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