Past Issues

In this study, the influence of classroom smells (foot odor, hair smell, sour smell, and sweat smell) of male high school and sweat smell and the offensive odor substance (ammonia and butyric acid) on the electroencephalography (EEG) activities of 20 female volunteers, ranging in age from 30 to 50 were studied. The representative response of brainwave index by these smells stimuli was most pronounced on temporal lobes among the brain lobes. By comparison with background EEG activities on temporal lobes, the smells reduced the relative alpha band power (0.04∼0.13) and increased the relative beta band power (0.02∼0.06) and the relative gamma band power (0.03∼0.09). The alpha wave was deactivated, high beta (18∼30Hz) and gamma (30∼50Hz) waves were remarkably activated. The order of EEG fluctuation caused by the smell stimulus is as follows; hair smell > butyric acid > foot odor, sour smell > ammonia > sweat smell. It means that the classroom smells cause an excessive brain arousal and straining and may be reducing one`s attention and learning ability.

keyword : 교실냄새, 발냄새, 머리냄새, 뇌파분석, 고등학생, classroom smells, foot odor, hair odor, EEG, male high school

According to increase of concerning in health and entry of aging society, sensor-based smart clothing has developed various type and applications. Sensor-based smart clothing should be designed with considering of the interaction between a human body-device-clothing, such as accuracy of signal, wearability, suitability and the configuration of the sensor and so on. In this respect, these characteristics distinguish sensor-based smart clothing process from clothing process and Sensor-based smart clothing process is expected to be needing requirements Specification for development purpose and interoperability assessment based on requirements engineering. In this study, to assess efficiency of process based on requirement engineering, the sensor-based smart clothing process was deducted in two types by analysis of empirical performance. Presented two process were empirically evaluated through qualitative and quantitative evaluation. As a result, design process Ⅱ based on requiments engineering were confirmed more effective process than processⅠ.

keyword : 스마트 의류, 디자인 프로세스, 요구사항 품질문서, 요구공학, 생체 신호 모니터링, Smart clothing, Design Process, requirements Specification, requirement engineering, vital sign monitoring

Attention is one of important cognitive functions in human affecting on the selectional concentration of relevant events and ignorance of irrelevant events. The discrimination of attentional and inattentional status is the first step to manage human`s attentional capability using computer assisted device. In this paper, we newly combine the non-linear recurrence pattern analysis and spectrum analysis to effectively extract features(total number of 13) from the electroencephalographic signal used in the input to classifiers. The performance of diverse types of attention-inattention classifiers, including supporting vector machine, back-propagation algorithm, linear discrimination, gradient decent, and logistic regression classifiers were evaluated. Among them, the support vector machine classifier shows the best performance with the classification accuracy of 81 %. The use of spectral band feature set alone(accuracy of 76 %) shows better performance than that of non-linear recurrence pattern feature set alone(accuracy of 67 %). The support vector machine classifier with hybrid combination of non-linear and spectral analysis can be used in later designing attention-related devices.

keyword : 집중, 분류기, 비선형분석, 스펙트럼분석, attention, classifier, non-linear analysis, spectrum analysis