Driver State and Behavior Detection Through Smart Wearables

Tavakoli, Arash; Kumar, Shashwat; Boukhechba, Mehdi; Heydarian, Arsalan

doi:10.1109/iv48863.2021.9575431

Cited by 14 publications

(4 citation statements)

References 29 publications

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“…Video and sensor data are integrated and used as heterogeneous data. For example, Tavakoli et al used heterogeneous data from in-car cameras and driver's wearable devices and used machine learning algorithms such as random forest to recognize driver activities such as using the phone, eating, crossing intersections, and highway driving [18]. Ranieri et al proposed an indoor action recognition method for the elderly using the RGB-D camera images captured by a humanoid robot, wearable inertial sensors, and surrounding sensors attached to items inside a home [19].…”

Section: Behavior Predictionmentioning

confidence: 99%

Enhanced Pet Behavior Prediction via S2GAN-Based Heterogeneous Data Synthesis

Kim,

Moon

2024

Applied Sciences

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Heterogeneous data have been used to enhance behavior prediction performance; however, it involves issues such as missing data, which need to be addressed. This paper proposes enhanced pet behavior prediction via Sensor to Skeleton Generative Adversarial Networks (S2GAN)-based heterogeneous data synthesis. The S2GAN model synthesizes the key features of video skeletons based on collected nine-axis sensor data and replaces missing data, thereby enhancing the accuracy of behavior prediction. In this study, data collected from 10 pets in a real-life-like environment were used to conduct recognition experiments on 9 commonly occurring types of indoor behavior. Experimental results confirmed that the proposed S2GAN-based synthesis method effectively resolves possible missing data issues in real environments and significantly improves the performance of the pet behavior prediction model. Additionally, by utilizing data collected under conditions similar to the real environment, the method enables more accurate and reliable behavior prediction. This research demonstrates the importance and utility of synthesizing heterogeneous data in behavior prediction, laying the groundwork for applications in various fields such as abnormal behavior detection and monitoring.

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Section: Behavior Predictionmentioning

confidence: 99%

Enhanced Pet Behavior Prediction via S2GAN-Based Heterogeneous Data Synthesis

Kim,

Moon

2024

Applied Sciences

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“…These [17], researchers used smartwatches to passively sense and classify driver activities, outside events, and road attributes. Analyzing data from 15 participants in a naturalistic driving study, they achieved impressive results with average F1 scores of 94.55%, 98.27%, and 97.86%, respectively, through 10-fold cross-validation.…”

Section: Literature Reviewmentioning

confidence: 99%

Elevating Driver Behavior Understanding With RKnD: A Novel Probabilistic Feature Engineering Approach

Shariful Islam,

Abu Tareq Rony,

Safran

et al. 2024

IEEE Access

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Early detection of driver behavior is a pivotal aspect in enhancing road safety, focusing on identifying and mitigating risky driving patterns before they lead to accidents. The use of smartphone sensors for data acquisition marks a significant advancement in this field. It allows for continuous, real-time monitoring of driving patterns without the need for specialized equipment. In this study, we leverage a publicly available smartphone motion sensor dataset, utilizing accelerometer and gyroscope data from a Samsung Galaxy S21 to analyze driving behaviors classified as slow, normal, and aggressive. This research introduces a novel feature engineering technique named the RKnD (Random forest, K-nearest classifier, Decision tree) probabilistic feature engineering technique, which integrates three prominent machine learning (ML) models. This blend offers a robust analysis of driver behavior, leveraging the strengths of each algorithm. This paper emphasizes the importance of data balancing in machine learning, employing the Synthetic Minority Oversampling Technique (SMOTE) to enhance the reliability of the predictions. Furthermore, k-fold crossvalidation is used to ensure the model's consistency and accuracy across original features and the proposed RKnD probabilistic features of the data sets. By achieving such high accuracy, the study demonstrates the potential of smartphone-based systems to significantly improve road safety. This paper introduces a novel approach utilizing smartphone motion sensor data to detect driver behaviors with a remarkable accuracy rate of 99.63%. This research stands out for its application of machine learning techniques in a practical, accessible manner. This pioneering approach named RKnD feature engineering sets a new standard in the realm of smart transportation systems, opening avenues for further innovations in the field, and filling a gap in road safety analysis to avoid road accidents. Future research on RKnD should streamline its algorithm for real-time use, diversify datasets, integrate advanced Deep Learning for complex pattern detection, and undertake real-world testing to validate practicality and uncover challenges.

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“…The key advantage of these techniques is that they may detect incidents instantaneously, allowing timely decisions to be made and damages to be minimized. Some examples of these techniques are: Vehicle Mounted Cameras [18], Smartphones Built-In Sensors [19,20], Specialized Hardware/Sensors [21], Advanced Driver Assistance Systems (ADAS) [22], etc.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight In-Vehicle Alcohol Detection using Smart Sensing and Supervised Learning

Al‐Haija¹,

Krichen²

2022

Preprint

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According to the risk investigations of being involved in an accident, alcohol-impaired driving is one of the major causes of motor-vehicles accidents. Preventing highly intoxicated persons from driving would potentially save many lives. This paper proposes a lightweight in-vehicle alcohol detection that processes the data generated from 6-alcohol sensors (MQ-3 Alcohol Sensors) using an optimizable shallow neural network (O-SNN). The experimental evaluation results exhibit a high-performance detection system scoring a 99.8% of detection accuracy with a very short inferencing delay of 2.22 µ seconds. Hence, the proposed model can be efficiently deployed and used to discover in-vehicle alcohol with high accuracy and low inference overhead as a part of the driver alcohol detection system for safety (DADSS) system aiming at massive deployment of alcohol sensing systems that could potentially save thousands of lives annually.

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Driver State and Behavior Detection Through Smart Wearables

Cited by 14 publications

References 29 publications

Enhanced Pet Behavior Prediction via S2GAN-Based Heterogeneous Data Synthesis

Enhanced Pet Behavior Prediction via S2GAN-Based Heterogeneous Data Synthesis

Elevating Driver Behavior Understanding With RKnD: A Novel Probabilistic Feature Engineering Approach

A Lightweight In-Vehicle Alcohol Detection using Smart Sensing and Supervised Learning

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