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RISS 인기검색어
- 검색키워드
- 저자 : Boon-Leng Lee (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Wearable Mobile-Based Emotional Response-Monitoring System for Drivers
Lee, Boon Giin,Chong, Teak Wei,Lee, Boon Leng,Park, Hee Joon,Kim, Yoon Nyun,Kim, Beomjoon IEEE 2017 IEEE transactions on human-machine systems Vol.47 No.5
<P>Negative emotional responses are a growing problem among drivers, particularly in countries with heavy traffic, and may lead to serious accidents on the road. Measuring stress-and fatigue-induced emotional responses by means of a wireless, wearable system would be useful for potentially averting roadway tragedies. The focus of this study was to develop and verify an emotional response-monitoring paradigm for drivers, derived from electromyography signals of the upper trapezius muscle, photoplethysmography signals of the earlobe, as well as inertialmotion sensing of the head movement. The relevant sensors were connected to a microcontroller unit equipped with a Bluetooth-enabled low-energy module, which allows the transmission of those sensor readings to a mobile device in real time. A mobile device application was then used to extract the data from the sensors and to determine the driver's current emotion status, via a trained support vector machine (SVM). The emotional response paradigm, tested in ten subjects, consisted of 10 min baseline, 5 min prestimulus, and 5 min poststimulus measurements. Emotional responses were categorized into three classes: relaxed, stressed, and fatigued. The analysis integrated a total of 36 features to train the SVM model, and the final stimulus results revealed a high accuracy rate (99.52%). The proposed wearable system could be applied to an intelligent driver's safety alert system, to use those emotional responses to prevent accidents affecting themselves and/or other innocent victims.</P>
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Standalone Wearable Driver Drowsiness Detection System in a Smartwatch
Lee, Boon-Leng,Lee, Boon-Giin,Chung, Wan-Young IEEE 2016 IEEE SENSORS JOURNAL Vol.16 No.13
<P>Drowsiness while driving is one of the main causes of fatal accidents, especially on monotonous routes such as highways. The goal of this paper is to design a completely standalone, distraction-free, and wearable system for driver drowsiness detection by incorporating the system in a smartwatch. The main objective is to detect the driver's drowsiness level based on the driver behavior derived from the motion data collected from the built-in motion sensors in the smartwatch, such as the accelerometer and the gyroscope. For this purpose, the magnitudes of hand movements are extracted from the motion data and are used to calculate the time, spectral, and phase domain features. The features are selected based on the feature correlation method. Eight features serve as an input to a support vector machine (SVM) classifier. After the SVM training and testing, the highest obtained accuracy was 98.15% (Karolinska sleepiness scale). This user-predefined system can be used by both left-handed and right-handed users, because different SVM models are used for different hands. This is an effective, safe, and distraction-free system for the detection of driver drowsiness.</P>
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Smartwatch-Based Wearable EEG System for Driver Drowsiness Detection
Gang Li,Boon-Leng Lee,Wan-Young Chung IEEE 2015 IEEE Sensors Journal Vol.15 No.12
<P>Driver drowsiness is a major cause of mortality in traffic accidents worldwide. Many physiological signals have been proposed to detect driver drowsiness. Among these signals, an electroencephalographic (EEG) signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many EEG-based driver drowsiness detection (DDD) models gained more and more attention in recent years. However, one limitation of these studies is that these models merely estimate discrete labels and, thus, did not allow for estimating the relative severity of driver drowsiness. This paper proposes a support vector machine-based posterior probabilistic model (SVMPPM) for DDD, aimed at transforming the drowsiness level to any value of 0~1 instead of discrete labels. A fully wearable EEG system which consists of a Bluetooth-enabled EEG headband and a commercial smartwatch was used to evaluate the proposed model in a real-time way. Twenty subjects who participated in a 1-h monotonous driving simulation experiment were used to develop this model with fifteen subjects for a building model and five subjects for a testing model. According to a video-based reference, the proposed system obtained an accuracy of 91.25% for an alert group (73 out of 80 data sets), 83.78% for an early-warning group (93 out of 111 data sets), and 91.92% for a full-warning group (91 out of 99 data sets). These results indicate that the combination of the proposed SVMPPM, the EEG headband, and the wrist-worn smart device constitutes an effective, simple, and inexpensive wearable solution for DDD.</P>
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