메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술저널
저자정보
Takuya Hida (Aoyama Gakuin University) Yuka Yoshioka (Tokyo Metropolitan University) Akihiko Seo (Tokyo Metropolitan University)
저널정보
대한산업공학회 Industrial Engineering & Management Systems Industrial Engineering & Management Systems Vol.18 No.1
발행연도
2019.3
수록면
78 - 88 (11page)
DOI
10.7232/iems.2019.18.1.078

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
Automated systems based on image processing have been introduced into the visual inspection process. However, owing to persisting technical and cost issues, human visual inspection still plays a major role in industrial inspections. When workers inspect small items (e.g., digital camera lenses or containers of liquid cosmetics) with one hand, which leads to an awkward posture, they experience upper-limb loading because of high loads on the upper extremities. This upper-limb loading may damage their hands, arms, and shoulders. There are a very limited number of studies that investigate the effects of upper-limb loading on humans during visual inspections; therefore, we conducted experiments on subjects where they used one hand to handle small objects for visual inspections. Using electromyography, joint angle measurements, and subjective evaluations, we investigated the effects of the grasp height and the subject’s inspection posture on the load applied to the upper limb. The results showed that the effects of upper-limb loading varied depending on the inspection posture because of variations in the locations where the loads acted on the body. Therefore, when evaluating upper-limb loading during such tasks, it was necessary to consider not only the muscle load but also the posture, the posture duration, and the subjective metrics.

목차

ABSTRACT
1. INTRODUCTION
2. METHODOLOGY
3. RESULTS AND DISCUSSION
4. CONCLUSIONS
REFERENCES

참고문헌 (25)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

이 논문과 함께 이용한 논문

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-ECN-0101-2019-530-000561300