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박상희,南文鉉 연세대학교 산업기술연구소 1974 논문집 Vol.5 No.1
The purpose of this research is to examine the ways in which eye position and eye movements are measured in order to justify the choice of method for the study concerning the eye movement control systems. The oculomoter control systems were reviewed previously as a view point of measuring objects. Because of the different aspects of eye movement which have been considered, various techniques have been developed each having its own characteristics of range, sensitivity, bandwidth, stability and ease of application. This study is directed at discussing the pros and cons inhernet in a basic method of measurement and to indicate the interesting historical development of oculography as new technologies were employed. Various methods are summarized and photo-electronic method (Limbus tracking technique) was chosen which is most available for the future experimentation. A new method is described which uses photo-electric matrix method to measure two-dimensional eye movements. This system is composed of the detector unit (monitor), matrix and correction circuits and stimulus generator. A light and small detector unit attached to the modified trial frame transduces positional informations and can be achieved head-mounting. The instrument operates in the infrared, so that it does not interfere with normal vision, and over a two-dimensional visual field to 15 degrees. The operating procedures are described and operating records are shown. Electrical and optical modifications of the present system will make possible the measurement of more smaller movement of the eye. Extensions and improvement of the technique of measurement are discussed briefly.
홍성우,김기환,남문현 世明大學校 1998 世明論叢 Vol.7 No.-
In this paper, veneral methods using autoregressive model inthe functional separation of the myoelecric signal of human arm movements are suggested. Covariance method and sequential least squares algorithm were used to determine the model parameters and the order of signal model to describe six arm movement patterns; the forearm flexion and extension, the wrist pronation adn supination, rotation in and rotation out. The confidence interval to classify the functions of arm movement was defined by the mean and standard deviation of total squares eror. With the error signals of sutoregressive(AR) model, the result showed that the highest success rate was obtained in the case of 4th order, and success rate was decreased with increase of order. This technique might be applied to biomedical-and rehabilitation engineering.