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Sungho Kim,Booyong Choi,Taehwan Cho,Yongkyun Lee,Hyojin Koo,Dongsoo Kim 대한인간공학회 2016 大韓人間工學會誌 Vol.35 No.5
Objective:This study aims to evaluate the features of heart rate variability (HRV) and respiratory signals as indices for a driver"s drowsiness and waking status in order to develop the classification model for a driver"s drowsiness and waking status using those features. Background: Driver"s drowsiness is one of the major causal factors for traffic accidents. This study hypothesized that the application of combined bio-signals to monitor the alertness level of drivers would improve the effectiveness of the classification techniques of driver"s drowsiness. Method: The features of three heart rate variability (HRV) measurements including low frequency (LF), high frequency (HF), and LF/HF ratio and two respiratory measurements including peak and rate were acquired by the monotonous car driving simulation experiments using the photoplethysmogram (PPG) and respiration sensors. The experiments were repeated a total of 50 times on five healthy male participants in their 20s to 50s. The classification model was developed by selecting the optimal measurements, applying a binary logistic regression method and performing 3-fold cross validation. Results: The power of LF, HF, and LF/HF ratio, and the respiration peak of drowsiness status were reduced by 38%, 22%, 31%, and 7%, compared to those of waking status, while respiration rate was increased by 3%. The classification sensitivity of the model using both HRV and respiratory features (91.4%) was improved, compared to that of the model using only HRV feature (89.8%) and that using only respiratory feature (83.6%). Conclusion: This study suggests that the classification of driver"s drowsiness and waking status may be improved by utilizing a combination of HRV and respiratory features. Application: The results of this study can be applied to the development of driver"s drowsiness prevention systems.
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고전검사이론과 문항반응이론을 이용한 사관학교 1차 선발 수학시험의 문항분석
강순부 ( Sunbu Kang ),이용균 ( Yongkyun Lee ),최부용 ( Booyong Choi ),이문식 ( Moonsik Lee ),김홍태 ( Hongtae Kim ) 공군사관학교 2016 空士論文集 Vol.67 No.1
본 연구의 목적은 사관학교 1차 선발시험 중에서 수학 과목에 대한 문항을 분석하여 조금 더 향상된 문항을 출제하는데 기여하려고 한다. 2011년도부터 2015년도까지의 5개년 동안 공군사관학교를 지원한 수험생 자료를 활용하여 문항분석을 실시하였다. 고전검사이론과 문항반응이론의 문항모수인 문항난이도, 문항변별도, 문항추측도를 이용하여 살펴본 결과 두 이론 모두에서 공통적으로 검토가 필요한 문항이 2개로 나타났다. 그 이외에도 고전검사이론에 의해 8개, 문항반응이론에 의해 4개의 문항이 수정 및 보완이 필요한 것으로 나타났다. The purpose of this study is conducive to academic exam in preparing improved items for future examinations, by analyzing the items provided in KAFA selection mathematical examination. This study utilized the information of past examinee of KAFA from 2011 to 2015. Through considerations from item parameters such as difficulty, discrimination and guessing, 2items which require review have been detected by both classical test theory and item response theory. Moreover, by means of classical test theory and item response theory, 8 and 4 items which need revision and supplementation have been discovered respectively.
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G-induced Loss of Consciousness(G-LOC) 예측을 위한 신체 부위별 Electromyogram(EMG) 신호 분석
김성호,김동수,조태환,이용균,최부용,Kim, Sungho,Kim, Dongsoo,Cho, Taehwan,Lee, Yongkyun,Choi, Booyong 한국군사과학기술학회 2017 한국군사과학기술학회지 Vol.20 No.1
G-induced Loss of Consciousness(G-LOC) can be predicted by measuring Electromyogram(EMG) signals. Existing studies have mainly focused on specific body parts and lacked of consideration with quantitative EMG indices. The purpose of this study is to analyze the indices of EMG signals by human body parts for monitoring G-LOC condition. The data of seven EMG features such as Root Mean Square(RMS), Integrated Absolute Value(IAV), and Mean Absolute Value(MAV) for reflecting muscle contraction and Slope Sign Changes(SSC), Waveform Length (WL), Zero Crossing(ZC), and Median Frequency(MF) for representing muscle contraction and fatigue was retrieved from high G-training on a human centrifuge simulator. A total of 19 trainees out of 47 trainees of the Korean Air Force fell into G-LOC condition during the training in attaching EMG sensor to three body parts(neck, abdomen, calf). IAV, MAV, WL, and ZC under condition after G-LOC were decreased by 17 %, 17 %, 18 %, and 4 % comparing to those under condition before G-LOC respectively. Also, RMS, IAV, MAV, and WL in neck part under condition after G-LOC were higher than those under condition before G-LOC; while, those in abdomen and calf part lower. This study suggest that measurement of IAV and WL by attaching EMG sensor to calf part may be optimal for predicting G-LOC.
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