Wearable Sensor System to Monitor the Status of the Automobile Drivers

Arjunan, Sridhar P.; Joseph, Arockia Vijay

doi:10.1007/978-981-16-5324-7_4

Cited by 1 publication

(1 citation statement)

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“…Similarly, in [23], the authors present a drowsiness detection model that fuses physiological (from ECG, respiration sensor, and camera), postural, and vehicle information. Recently, Arjunan et al [28] proposed a monitoring system that uses the combination of data from multiple wearable sensors, namely blood pressure, heart rate, blood oxygen, body temperature, EEG, and electromyography (EMG) sensors, to detect the status of the driver.…”

Section: Drowsiness Detection Based On Physiological Signalsmentioning

confidence: 99%

Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance

Amidei

Spinsante

Iadarola

et al. 2023

Sensors

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The majority of car accidents worldwide are caused by drowsy drivers. Therefore, it is important to be able to detect when a driver is starting to feel drowsy in order to warn them before a serious accident occurs. Sometimes, drivers are not aware of their own drowsiness, but changes in their body signals can indicate that they are getting tired. Previous studies have used large and intrusive sensor systems that can be worn by the driver or placed in the vehicle to collect information about the driver’s physical status from a variety of signals that are either physiological or vehicle-related. This study focuses on the use of a single wrist device that is comfortable for the driver to wear and appropriate signal processing to detect drowsiness by analyzing only the physiological skin conductance (SC) signal. To determine whether the driver is drowsy, the study tests three ensemble algorithms and finds that the Boosting algorithm is the most effective in detecting drowsiness with an accuracy of 89.4%. The results of this study show that it is possible to identify when a driver is drowsy using only signals from the skin on the wrist, and this encourages further research to develop a real-time warning system for early detection of drowsiness.

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Section: Drowsiness Detection Based On Physiological Signalsmentioning

confidence: 99%