Abnormal Behavior Detection Based on Smartphone Sensors

Lu, Dang-Nhac; Tran, Thuy-Binh; Nguyen, Thi-Hau; Nguyen, Ha-Nam

doi:10.1007/978-3-319-77818-1_19

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Recently, researchers have paid a lot of attention to study various methods of providing assistance and safety awareness to drivers. Indeed, such works primarily fall into the following categories: recognizing vehicle mode (car, bus, train, bike, walking ...) [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ], identifying driving styles (normal, aggressive, drunken, fatigue, drowsy, inattentive ...) [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ], detecting normal/abnormal driving events (moving, stopping, turning left, turning right, weaving, sudden braking, fast u-turn...) [ 17 , 18 , 19 , 20 , 21 , 22 ], accident detection [ 23 , 24 ], estimating energy consumption and pollution [ 25 ], monitoring road and traffic condition [ 26 , 27 , 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the global status reports on road safety 2015 by WHO also shows that approximately a quarter of all road traffic deaths involve in motorcyclists. However, very few existing works provide driving assistance and safety awareness for motorcyclists [ 4 , 20 , 21 , 22 , 32 ]. Nonetheless, there are certain limitations in such works.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones

Nguyen

2018

Sensors

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In this paper, we present a flexible combined system, namely the Vehicle mode-driving Activity Detection System (VADS), that is capable of detecting either the current vehicle mode or the current driving activity of travelers. Our proposed system is designed to be lightweight in computation and very fast in response to the changes of travelers’ vehicle modes or driving events. The vehicle mode detection module is responsible for recognizing both motorized vehicles, such as cars, buses, and motorbikes, and non-motorized ones, for instance, walking, and bikes. It relies only on accelerometer data in order to minimize the energy consumption of smartphones. By contrast, the driving activity detection module uses the data collected from the accelerometer, gyroscope, and magnetometer of a smartphone to detect various driving activities, i.e., stopping, going straight, turning left, and turning right. Furthermore, we propose a method to compute the optimized data window size and the optimized overlapping ratio for each vehicle mode and each driving event from the training datasets. The experimental results show that this strategy significantly increases the overall prediction accuracy. Additionally, numerous experiments are carried out to compare the impact of different feature sets (time domain features, frequency domain features, Hjorth features) as well as the impact of various classification algorithms (Random Forest, Naïve Bayes, Decision tree J48, K Nearest Neighbor, Support Vector Machine) contributing to the prediction accuracy. Our system achieves an average accuracy of 98.33% in detecting the vehicle modes and an average accuracy of 98.95% in recognizing the driving events of motorcyclists when using the Random Forest classifier and a feature set containing time domain features, frequency domain features, and Hjorth features. Moreover, on a public dataset of HTC company in New Taipei, Taiwan, our framework obtains the overall accuracy of 97.33% that is considerably higher than that of the state-of the art.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones

Nguyen

2018

Sensors

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“…For instance, Meseguer et al in 2015 used an ANN aimed to classify aggressive, normal, and quiet driving styles using speed, acceleration, and RPM data collected from in-vehicle sensors [15]. Lu et al in 2017 used different classifiers, including ANNs, to identify abnormal driving behaviors starting from smartphone sensors' signals [16]. Cheng et al in 2018 presented an ANN trained by means features extracted by acceleration, pedal angle, and speed signals gathered from a car simulator to classify drivers into aggressive, normal, and calm [17].…”

Section: Related Workmentioning

confidence: 99%

Improving Machine Learning Identification of Unsafe Driver Behavior by Means of Sensor Fusion

Lattanzi¹,

Castellucci²,

Freschi³

2020

Applied Sciences

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Most road accidents occur due to human fatigue, inattention, or drowsiness. Recently, machine learning technology has been successfully applied to identifying driving styles and recognizing unsafe behaviors starting from in-vehicle sensors signals such as vehicle and engine speed, throttle position, and engine load. In this work, we investigated the fusion of different external sensors, such as a gyroscope and a magnetometer, with in-vehicle sensors, to increase machine learning identification of unsafe driver behavior. Starting from those signals, we computed a set of features capable to accurately describe the behavior of the driver. A support vector machine and an artificial neural network were then trained and tested using several features calculated over more than 200 km of travel. The ground truth used to evaluate classification performances was obtained by means of an objective methodology based on the relationship between speed, and lateral and longitudinal acceleration of the vehicle. The classification results showed an average accuracy of about 88% using the SVM classifier and of about 90% using the neural network demonstrating the potential capability of the proposed methodology to identify unsafe driver behaviors.

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Enhancement in identification of unsafe driving behaviour by blending machine learning and sensors

Malik¹,

Nandal²,

Maan

et al. 2022

Int J Syst Assur Eng Manag

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Abnormal Behavior Detection Based on Smartphone Sensors

Cited by 3 publications

References 11 publications

Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones

Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones

Improving Machine Learning Identification of Unsafe Driver Behavior by Means of Sensor Fusion

Enhancement in identification of unsafe driving behaviour by blending machine learning and sensors

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