Smart Fatigue Phone: Real-time estimation of driver fatigue using smartphone-based cortisol detection

Shin, Jong‐Shik; Kim, Soocheol; Yoon, Taehee; Joo, Chulmin; Jung, Hyo Il

doi:10.1016/j.bios.2019.04.046

Cited by 29 publications

(11 citation statements)

References 18 publications

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“…In recent years, deep learning techniques, such as recurrent neural network (RNN) [19,20], convolutional neural network (CNN) [7,19], generative adversarial network (GAN) [21], long short-term memory (LSTM) network [20,21], have found their use in classifying the state of the driver based on various signals, such as electroencephalography (EEG) [19,20,22,23], images [20] and EOG [21].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Drivers’ Activity Based on 1D Convolutional Neural Network

et al. 2020

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Background and objective: Driving a car is a complex activity which involves movements of the whole body. Many studies on drivers’ behavior are conducted to improve road traffic safety. Such studies involve the registration and processing of multiple signals, such as electroencephalography (EEG), electrooculography (EOG) and the images of the driver’s face. In our research, we attempt to develop a classifier of scenarios related to learning to drive based on the data obtained in real road traffic conditions via smart glasses. In our approach, we try to minimize the number of signals which can be used to recognize the activities performed while driving a car. Material and methods: We attempt to evaluate the drivers’ activities using both electrooculography (EOG) and a deep learning approach. To acquire data we used JINS MEME smart glasses furnished with 3-point EOG electrodes, 3-axial accelerometer and 3-axial gyroscope. Sensor data were acquired on 20 drivers (ten experienced and ten learner drivers) on the same 28.7 km route under real road conditions in southern Poland. The drivers performed several tasks while wearing the smart glasses and the tasks were linked to the signal during the drive. For the recognition of four activities (parking, driving through a roundabout, city traffic and driving through an intersection), we used one-dimensional convolutional neural network (1D CNN). Results: The maximum accuracy was 95.6% on validation set and 99.8% on training set. The results prove that the model based on 1D CNN can classify the actions performed by drivers accurately. Conclusions: We have proved the feasibility of recognizing drivers’ activity based solely on EOG data, regardless of the driving experience and style. Our findings may be useful in the objective assessment of driving skills and thus, improving driving safety.

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Section: Introductionmentioning

confidence: 99%

Recognition of Drivers’ Activity Based on 1D Convolutional Neural Network

et al. 2020

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Section: Resultsmentioning

confidence: 99%

“…These types of applications are extremely different in terms of the variety of chemical structures to be analyzed (samples) and the functionality of the smartphone application used to process the samples. [161][162][163][164][165][166][167] Imaging techniques are not popular in this category, as the adapter consists of special electronics that are designed specifically to target a unique chemical compound. [168][169][170][171][172][173][174] Using a smartphone proved to be cost effective (see Figure 12), and extremely accurate results were reported when comparing the performance of the smartphone-based adapters to those of laboratory equipment and devices with a low CV%, a high regression coefficient, lower costs, and a low LOD (see Figures 10 to 13).…”

Section: Electrochemical Applicationsmentioning

confidence: 99%

A review of smartphone point‐of‐care adapter design

Alawsi

Al-Bawi

2019

Engineering Reports

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In this review, we explore the potential of smartphone‐based applications based on their unique ability to support portable, easy‐to‐use, precise, and efficient functions, which, in turn, makes lab‐on‐hardware a trending area of novel research. Smartphones can assist surgeons, physicians, biologists, chemists, ophthalmologists, and laboratory technicians in maintaining an easy‐to‐use, cost‐effective, and integrated environment for treatment, diagnosis, and point‐of‐care (POC) applications. These POC applications can improve patients' quality of life, offering patients a precise diagnosis and the correct treatment. This is a noble goal that aims to reduce costs and to increase the accuracy of sample testing, treatment, and diagnosis. Recent innovations have made major advances in smartphone adapters, providing portability, robustness, self‐powered devices, small‐sized adapters, and ease of usage. Lab‐on‐hardware is a progressing field, and smartphone imaging applications are increasingly expanding, resulting in POC applications with the latest and most advanced image analysis, enhancement, recognition, and other image processing techniques. The most recent studies in the field are explored to provide a solid background to interested researchers against which they can choose of application and/or adapter design they aim to develop, with a discussion of the methods reviewed here.

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“…introduced the Smart Fatigue Phone, in which a smartphone‐linked fluorescence signal reader was used to quantify the signal of a lateral flow immunosensor and measure the salivary cortisol concentration, and further recognized the fatigue status. [ 50 ]…”

Section: Noninvasive Biosensors and Collected Physiological Informationmentioning

confidence: 99%

Machine Learning‐Reinforced Noninvasive Biosensors for Healthcare

Zhang

Wang

Liu

et al. 2021

Adv Healthcare Materials

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The emergence and development of noninvasive biosensors largely facilitate the collection of physiological signals and the processing of health‐related data. The utilization of appropriate machine learning algorithms improves the accuracy and efficiency of biosensors. Machine learning‐reinforced biosensors are started to use in clinical practice, health monitoring, and food safety, bringing a digital revolution in healthcare. Herein, the recent advances in machine learning‐reinforced noninvasive biosensors applied in healthcare are summarized. First, different types of noninvasive biosensors and physiological signals collected are categorized and summarized. Then machine learning algorithms adopted in subsequent data processing are introduced and their practical applications in biosensors are reviewed. Finally, the challenges faced by machine learning‐reinforced biosensors are raised, including data privacy and adaptive learning capability, and their prospects in real‐time monitoring, out‐of‐clinic diagnosis, and onsite food safety detection are proposed.

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Smart Fatigue Phone: Real-time estimation of driver fatigue using smartphone-based cortisol detection

Cited by 29 publications

References 18 publications

Recognition of Drivers’ Activity Based on 1D Convolutional Neural Network

Recognition of Drivers’ Activity Based on 1D Convolutional Neural Network

A review of smartphone point‐of‐care adapter design

Machine Learning‐Reinforced Noninvasive Biosensors for Healthcare

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