인지부하를 분석하고 이해하는 것은 HCI(Human-Computer Interaction) 기술을 향상시키며 사고를 예방하는 중요 기술이다. 최근 인지부하의 분석에는 심박변이도 분석이 많이 이용되고 있으며, 웨어러블 기기의 사용이 보편화됨에 따라 Photoplethysmogram의 맥박변이도를 심박변이도 대신 사용하는 추세이다. 그러나, 맥박변이도와 심박변이도는 모든 경우에 대해 호환이 불가능하며 이들을 같은 알고리즘으로 분석하는 것은 정확도의 오차를 증가시킨다. 이에 본 연구는 각 파형의 중요 파라미터를 도출하여 두 파형 사이의 오차를 줄이고, 전체적인 정확도를 높이는 것이 목표이다. 주파수 영역, 시간 영역, 비선형 영역의 3가지 영역에서 총 37가지의 파라미터를 추출하여 XAI(explainable artificial interllingence)를 사용한 결과, 최대 73.8%의 정확도를 얻었으며 오차도 최대 1%미만으로 줄일 수 있었다. 또한, 추가적으로 t-test를 시행하여 파형 별 중요 파라미터를 분석한 결과, 심박변이도 분석에는 SD1/SD2가 중요한 파라미터이며, 맥박변이도 분석에는 NN20, pNN20이 중요한 파라미터임을 알 수 있었다.
KSP Keywords
human computer interaction(HCI)
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