Driver Distraction Recognition Using Wearable IMU Sensor Data

Sun, Wencai; Si, Yihao; Guo, Mengzhu; Li, Shiwu

doi:10.3390/su13031342

Cited by 11 publications

(11 citation statements)

References 29 publications

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“…Note that the categories in this graph correspond to Table 2 . Also, the study of drowsiness driving using body-worn sensors, which are referred to in this paper, can be found in the literature [ 15 , 17 , 23 , 40 ], the study of distracted driving, in [ 24 , 25 , 26 ], and the study of driving in the absent-minded state, in [ 3 ] and this study.…”

Section: Figurementioning

confidence: 99%

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Inattentive Driving Detection Using Body-Worn Sensors: Feasibility Study

Akiduki

Nagasawa

Zhang

et al. 2022

Sensors

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This study aims to build a system for detecting a driver’s internal state using body-worn sensors. Our system is intended to detect inattentive driving that occurs during long-term driving on a monotonous road, such as a high-way road. The inattentive state of a driver in this study is an absent-minded state caused by a decrease in driver vigilance levels due to fatigue or drowsiness. However, it is difficult to clearly define these inattentive states because it is difficult for the driver to recognize when they fall into an absent-minded state. To address this problem and achieve our goal, we have proposed a detection algorithm for inattentive driving that not only uses a heart rate sensor, but also uses body-worn inertial sensors, which have the potential to detect driver behavior more accurately and at a much lower cost. The proposed method combines three detection models: body movement, drowsiness, and inattention detection, based on an anomaly detection algorithm. Furthermore, we have verified the accuracy of the algorithm with the experimental data for five participants that were measured in long-term and monotonous driving scenarios by using a driving simulator. The results indicate that our approach can detect both the inattentive and drowsiness states of drivers using signals from both the heart rate sensor and accelerometers placed on wrists.

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Section: Figurementioning

confidence: 99%

“…However, these methods require cameras or in-vehicle seat sensors to detect driver behavior. On the other hand, wearable sensors, such as wrist-worn accelerometers, have the potential to detect driver behavior more accurately and at a much lower cost [ 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Inattentive Driving Detection Using Body-Worn Sensors: Feasibility Study

Akiduki

Nagasawa

Zhang

et al. 2022

Sensors

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“…is method can be used in the ERP system to make up for the defect that the traditional ERP system cannot carry out data balance. Sun et al [19] combined with the actual project, focused on the data optimization of the data distribution of the shared equipment, and the reasonable planning of the workshop production, so that the physiological index system can meet the management goals of various physiological indicators, and studied and analyzed the multilevel physiological indicators of the enterprise. e planning model, combined with the physiological index ability balance and plan control method with the goal of delivering on time, developed the physiological index ability balance and plan control system.…”

Section: Related Workmentioning

confidence: 99%

Monitoring and Visualization of Physical Exercise Physiological Indicators Driven by Discrete Data

Tang

2022

Scientific Programming

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Physical exercise physiological index monitoring has a wide range of applications in the fields of physiological index planning and design and organizational network evolution. Among the existing analysis methods for monitoring data points of physical exercise physiological indicators, the analysis error of point events under linear constraints is relatively large. Based on discrete data-driven datasets, this paper realizes the monitoring and visualization of sports physiological indicators. First, the principal component analysis of multivariate discrete data is used for dimensionality reduction. Second, the clustering of discrete physical exercise data uses the BIC criterion to preset the number of clusters, and the R software is used to visually realize the clustering results of physical exercise physiological indicators in each region in the text. The experiment solves the problem of mismatch of model parameter combinations when the physical exercise index monitoring quantity is used for the auxiliary analysis of the clustering results. Through the ARI index monitoring, the accuracy of the clustering physical exercise results of the method is increased to 89.7%, and the error rate is controlled within 4.3%. It promotes the superiority and effectiveness of multivariate discrete data-driven model clustering methods.

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“…Monitor correct cognitive and safe driver behaviors with intelligent sensors and IoT to monitor the face, eyes, or movements of a driver's entire body for a novel driver education process [15].…”

Section: Introductionmentioning

confidence: 99%

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Doniec¹,

Konior²,

Sieciński³

et al. 2023

Preprint

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To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring the cognitive capabilities of drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to primary activities in driving, including crossroad, parking, roundabout was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93–0.94). Furthermore, using the same algorithm, it was possible to distinguish four secondary activities related to activities of daily life and secondary when driving a car.

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Driver Distraction Recognition Using Wearable IMU Sensor Data

Cited by 11 publications

References 29 publications

Inattentive Driving Detection Using Body-Worn Sensors: Feasibility Study

Inattentive Driving Detection Using Body-Worn Sensors: Feasibility Study

Monitoring and Visualization of Physical Exercise Physiological Indicators Driven by Discrete Data

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

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