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2021 | OriginalPaper | Buchkapitel

Driver Multi-function Safety Assistance System

verfasst von : Wanqi Wang, Peidong Zhuang, Shiwen Zhang

Erschienen in: Communications, Signal Processing, and Systems

Verlag: Springer Singapore

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Abstract

With the increasing number of motor vehicles, the problem of traffic accidents follows. The survey shows that fatigue driving and heart diseases have become important causes of traffic safety accidents. In addition to real-time monitoring of the driver’s physiological health, the design also uses the electrocardiogram and grip strength signals to determine the driver’s fatigue level. Based on the important physiological information contained in the electrocardiogram signal and the basis of the time domain and time–frequency domain feature transformation of the grip strength signal, the fatigue characteristics were analyzed. The design uses STM32 as the control core, the steering wheel cover as the medium, and built-in electrode chips and pressure sensors to collect real-time ECG signals to determine the physiological health of the driver; at the same time, the ECG signal and the grip signal are analyzed at the same time, and the fatigue of the driver is analyzed. Real-time monitoring of the degree ensures the accuracy and stability of the system. In addition, the Bluetooth module is connected to the mobile phone terminal to realize the wireless communication function, and the terminal monitors various indicators of the driver’s body and the degree of fatigue in real time. When the body changes suddenly, the danger level is predicted, and the voice reminds the driver; and when the danger level is high, the satellite accurately locates and transmits the position to the family and the hospital in time, which is convenient for finding and timely rescue. When the driver feels tired or drowsy, the mobile phone terminal reminds or wakes up the driver through voice to realize the human–computer interaction function and prevent the occurrence of traffic accidents.

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Literatur
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Zurück zum Zitat Patel M, Lal SKL, Kavanagh D et al (2011) Applying neural network analysis on heartrate variability data to assess driver fatigue. Expert Syst Appl Patel M, Lal SKL, Kavanagh D et al (2011) Applying neural network analysis on heartrate variability data to assess driver fatigue. Expert Syst Appl
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Zurück zum Zitat . Li F, Wang XW, Lu J (2013) Detection of driving fatigue based on grip force on steering wheel with wavelet transformation and support vector machine. In: Neural information processing, Daegu, Springer, Berlin, Heidelberg, pp 141–148 . Li F, Wang XW, Lu J (2013) Detection of driving fatigue based on grip force on steering wheel with wavelet transformation and support vector machine. In: Neural information processing, Daegu, Springer, Berlin, Heidelberg, pp 141–148
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Metadaten
Titel
Driver Multi-function Safety Assistance System
verfasst von
Wanqi Wang
Peidong Zhuang
Shiwen Zhang
Copyright-Jahr
2021
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-15-8411-4_129

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