• 정보화정책
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• 제27권3호
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• pp.39-55
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• 2020

본 연구는 중고령자의 디지털기기 이용동기, 디지털기기 이용태도, 디지털기기 이용성과의 요인 간 관계를 검증하기 위해 경로분석을 진행하였다. 실증분석을 위해, National Information Society Agency의 2018 디지털정보격차 실태조사를 사용하였으며 55세 이상 전체 조사 대상자 2,303명 중 결측치를 제거하여 1,664명을 최종분석 하였다. 구조모형의 경로계수를 살펴본 결과, 디지털기기 이용동기는 디지털기기 이용태도(표준요인 부하랑=0.847)에, 디지털기기 이용태도는 디지털기기 이용성과(표준요인 부하랑=0.745)에 통계적으로 유의한 영향을 미치는 것으로 나타났다. 본 연구는 디지털기기 이용동기가 바로 디지털기기 이용성과를 높이지는 않으나, 디지털기기 이용태도를 높이고, 디지털기기 이용태도는 디지털기기 이용성과를 높일 수 있음을 시사하였으며 중고령자의 디지털기기 이용을 활성화하기 위한 실천적·정책적 함의를 제시하였다는 점에 의의가 있다.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권11호
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• pp.502-510
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• 2006

In this paper, we describe implementation of a computer access device for the severly motor-disability. Many people with severe motor disabilities need an augmentative communication technology. Those who are totally paralyzed, or 'locked-in' cannot use conventional augmentative technologies, all of which require some measure of muscle control. The forehead is often the last site to suffer degradation in cases of severe disability and degenerative disease. For example, In ALS(Amyotrophic Lateral Sclerosis) and MD(Muscular dystrophy) the ocular motorneurons and ocular muscles are usually spared permitting at least gross eye movements, but not precise eye pointing. We use brain and body forehead bio-potentials in a novel way to generate multiple signals for computer control inputs. A bio-amplifier within this device separates the forehead signal into three frequency channels. The lowest channel is responsive to bio-potentials resulting from an eye motion, and second channel is the band pass derived between 0.5 and 45Hz, falling within the accepted Electroencephalographic(EEG) range. A digital processing station subdivides this region into eleven components frequency bands using FFT algorithm. The third channel is defined as an Electromyographic(EMG) signal. It responds to contractions of facial muscles and is well suited to discrete on/off switch closures, keyboard commands. These signals are transmitted to a PC that analyzes in a time series and a frequency region and discriminates user's intentions. That software graphically displays user's bio-potential signals in the real time, therefore user can see their own bio-potentials and control their physiological signals little by little after some training sessions. As a result, we confirmed the performance and availability of the developed system with experimental user's bio-potentials.