A driving behavior detection system based on a smartphone's built‐in sensor

Yan-tao, LI; Xue, Fengtao; Feng, Lihui; Qu, Zehui

doi:10.1002/dac.3178

Cited by 28 publications

(11 citation statements)

References 18 publications

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“…The counting module is configured to compare the data value of the inflection point with a preset threshold value in the calibration module and count the number of data values of the inflection point that exceeds a preset threshold [23][24][25].…”

Section: Experimental Data Collectionmentioning

confidence: 99%

Method of Measuring Pull-up Times Based on Built-in Acceleration Sensor in Mobile Terminal

Tong¹,

Wang²

2021

DSA

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With the increasingly perfect functions of smart mobile terminals, mobile terminal devices have become an indispensable part of people's lives. Therefore, based on the existing mobile terminal equipment, the development of fitness detection applications can provide more convenience for people's lives. This article aims to study the method of measuring the number of pull-up times of a mobile terminal with a built-in acceleration sensor. This paper uses a 3-axis acceleration sensor of the mobile terminal as a measurement device. By analyzing the acceleration of each axis of the cycle-type repetitive motion of the pull-up, The value is transformed, the collected sensor data is processed and analyzed, then the motion is matched, and the pull-up movement is counted. By analyzing the periodic repetitive motion of the pull-up, the transformation of the acceleration value of each axis of the sensor is analyzed. The collected sensor data is processed and analyzed by a mobile terminal, such as a mobile phone, and then motion matching is performed to count the pull-up motion. Validate the mobile terminal through actual experiments and collect experimental data. The experimental data show that the method of measuring the number of pullup times of the mobile terminal with a built-in acceleration sensor meets the actual requirements, and the calculation accuracy is improved by 65.2%. Experimental data show that under the development of mobile Internet, the method of measuring pullup times of mobile terminals with built-in acceleration sensors can meet the needs of users to count their exercise times anytime, anywhere. It is statistically efficient, convenient and economical. In terms of time, it saves about 35% of the time compared with traditional calculation and statistical methods.

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Section: Experimental Data Collectionmentioning

confidence: 99%

Method of Measuring Pull-up Times Based on Built-in Acceleration Sensor in Mobile Terminal

Tong¹,

Wang²

2021

DSA

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“…[15] presented a novel driving behavior recognition system that can detect brakes, accelerations, turns, and lane-change with high accuracy. Health Driving [16] is a mobile-based application that detects driving events and conditions when accelerometer axis values pass predefined thresholds. It assigns classes of safe/unsafe to detected events.…”

Section: Related Workmentioning

confidence: 99%

AutoCoach: An Intelligent Driver Behavior Feedback Agent with Personality-Based Driver Models

et al. 2021

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Nowadays, AI has many applications in everyday human activities such as exercise, eating, sleeping, and automobile driving. Tech companies can apply AI to identify individual behaviors (e.g., walking, eating, driving), analyze them, and offer personalized feedback to help individuals make improvements accordingly. While offering personalized feedback is more beneficial for drivers, most smart driver systems in the current market do not use it. This paper presents AutoCoach, an intelligent AI agent that classifies drivers’ into different driving-personality groups to offer personalized feedback. We have built a cloud-based Android application to collect, analyze and learn from a driver’s past driving data to provide personalized, constructive feedback accordingly. Our GUI interface provides real-time user feedback for both warnings and rewards for the driver. We have conducted an on-the-road pilot user study. We conducted a pilot study where drivers were asked to use different agent versions to compare personality-based feedback versus non-personality-based feedback. The study result proves our design’s feasibility and effectiveness in improving the user experience when using a personality-based driving agent, with 61% overall acceptance that it is more accurate than non-personality-based.

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“…Publications Accelerometer [167]- [172] Accelerometer, GNSS [71], [121], [173]- [177] Accelerometer, GNSS, Magnetometer [53], [99], [178]- [180] Gyroscope [181] IMU [122], [140], [182] IMU, GNSS [13], [54], [183], [184] IMU, Magnetometer [185], [186] IMU, GNSS, Magnetometer [187], [188] discuss supplementary information sources and how these can be used to improve the navigation solution.…”

Section: Sensorsmentioning

confidence: 99%

“…Due to the high costs associated with identifying and analyzing road deteriorations using specialized vehicles and equipment, the collection of crowdsourced data from smartphones in passenger vehicles has emerged as an attractive alternative [277]. Typically, related studies will threshold the standard deviation of measurements from smartphone-embedded accelerometers to detect potholes, cracks, speed bumps, or other road anomalies (with accompanying position estimates used to mark their location) [122], [172], [185], [241], [249], [278]- [285]. Nonetheless, many variations to this approach have been presented.…”

Section: Road Condition Monitoringmentioning

confidence: 99%

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Wahlström

Skog

Händel

2017

IEEE Trans. Intell. Transport. Syst.

157

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Abstract-Just like it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on userfriendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, cloud computing, vehicular ad hoc networks, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphonebased automotive navigation, and survey the state-of-the-art in smartphone-based transportation mode classification, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment.

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A driving behavior detection system based on a smartphone's built‐in sensor

Cited by 28 publications

References 18 publications

Method of Measuring Pull-up Times Based on Built-in Acceleration Sensor in Mobile Terminal

Method of Measuring Pull-up Times Based on Built-in Acceleration Sensor in Mobile Terminal

AutoCoach: An Intelligent Driver Behavior Feedback Agent with Personality-Based Driver Models

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

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