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- 저자 : Teak-Wei Chong (검색결과 4 건)
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딥러닝 방식의 웨어러블 센서를 사용한 미국식 수화 인식 시스템
정택위(Teak-Wei Chong),김범준(Beom-Joon Kim) 한국전자통신학회 2020 한국전자통신학회 논문지 Vol.15 No.2
수화는 청각 장애인이 다른 사람들과 의사소통할 수 있도록 설계된 것이다. 그러나 수화는 충분히 대중화되어 있지 않기 때문에 청각 장애인이 수화를 통해서 일반 사람들과 원활하게 의사소통하는 것은 쉽지 않은 문제이다. 이러한 문제점에 착안하여 본 논문에서는 웨어러블 컴퓨팅 및 딥러닝 기반 미국식 수화인식 시스템을 설계하고 구현하였다. 이를 위해서 본 연구에서는 손등과 손가락에 장착되는 총 6개의 IMUs(Inertial Measurement Unit) 센서로 구성된 시스템을 구현하고 이를 이용한 실험을 수행하여 156개 특징이 수집된 데이터 추출을 통해서 총 28개 단어에 대한 미국식 수화 인식 방법을 제안하였다. 특히 LSTM (Long Short-Term Memory) 알고리즘을 사용하여 최대 99.89%의 정확도를 달성할 수 있었고 향후 청각 장애인들의 의사소통에 큰 도움이 될 것으로 예상된다. Sign language was designed for the deaf and dumb people to allow them to communicate with others and connect to the society. However, sign language is uncommon to the rest of the society. The unresolved communication barrier had eventually isolated deaf and dumb people from the society. Hence, this study focused on design and implementation of a wearable sign language interpreter. 6 inertial measurement unit (IMU) were placed on back of hand palm and each fingertips to capture hand and finger movements and orientations. Total of 28 proposed word-based American Sign Language were collected during the experiment, while 156 features were extracted from the collected data for classification. With the used of the long short-term memory (LSTM) algorithm, this system achieved up to 99.89% of accuracy. The high accuracy system performance indicated that this proposed system has a great potential to serve the deaf and dumb communities and resolve the communication gap.
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METHODS TO DETECT AND REDUCE DRIVER STRESS: A REVIEW
정완영,Teak-Wei Chong,Boon-Giin Lee 한국자동차공학회 2019 International journal of automotive technology Vol.20 No.5
Automobiles are the most common modes of transportation in urban areas. An alert mind is a prerequisite while driving to avoid tragic accidents; however, driver stress can lead to faulty decision-making and cause severe injuries. Therefore, numerous techniques and systems have been proposed and implemented to subdue negative emotions and improve the driving experience. Studies show that conditions such as the road, state of the vehicle, weather, as well as the driver’s personality, and presence of passengers can affect driver stress. All the above-mentioned factors significantly influence a driver’s attention. This paper presents a detailed review of techniques proposed to reduce and recover from driving stress. These technologies can be divided into three categories: notification alert, driver assistance systems, and environmental soothing. Notification alert systems enhance the driving experience by strengthening the driver’s awareness of his/her physiological condition, and thereby aid in avoiding accidents. Driver assistance systems assist and provide the driver with directions during difficult driving circumstances. The environmental soothing technique helps in relieving driver stress caused by changes in the environment. Furthermore, driving maneuvers, driver stress detection, driver stress, and its factors are discussed and reviewed to facilitate a better understanding of the topic.
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Stress Events Detection of Driver by Wearable Glove System
Lee, Dae Seok,Chong, Teak Wei,Lee, Boon Giin IEEE 2017 IEEE Sensors Journal Vol. No.
<P>This paper is focused to develop a wearable glove system to detect driver stress events in real time. The driver's stress is estimated by the use of physiological signals and steering wheel motion analysis. The steering wheel motion is analyzed by driver's hand moving characteristic. Principally, the sensors on the glove gathered the photoplethysmogram signal via fingertip, and hand motion signal via inertial motion unit. The sensor module readings are transmitted to an end terminal application via a Bluetooth low energy transmission module to compute the driver stress index. The studies are carried out in a simulated driving, which is composed of three distinct driving scenarios to study the subjects' behaviors that correlate with stress. Twenty-eight subjects are requested to perform three different driving sessions with random scenarios generated while performing various driving maneuvers to assess the dynamic of mental workloads. The stress assessments of driving test subjects are self-reported at pre- and post-stimulus as well as observed through facial expression recorded throughout the whole experiments. Moreover, this paper also aimed to investigate the correlation of stress events with different driving tasks. Stress index is computed by a support vector machine pattern classifier with extracted features from sensors reading. Notably, stress index differences were found among three driving scenarios and driving maneuvers. Results revealed the true accuracy of stress detection is greater than 95% in average.</P>
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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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