Earticle

초록

영어

A lot of construction works are done by manually and often involves heavy materials handling, which increases the risk of musculoskeletal disorders. However, monitoring physical load levels applied to workers during construction work is difficult due to the large size of the site and a huge number of workforce. Under this circumstance, this study developed an approach to evaluate the lifting workload of construction workers using a smart insole sensor for the purpose of preventing musculoskeletal disorders. In the experiment, different level of risks were set by changing the lifting load according to the NIOSH Lifting Index. Participants wore a smart insole and performed repetitive lifting tasks. A analysis was conducted by applying the Bi-LSTM model, a deep learning algorithm based on a recurrent neural network. As a result of the analysis, an accuracy of up to 84.1% was confirmed when using data collected from the nearest foot to the lifting object. The approach introduced in this study utilizes foot-pressure data which is easier to acquire than other biometric data and would have a higher field applicability. The approach would help to manage the level of physical load during a heavy material handling tasks at construction sites and prevent musculoskeletal disorders of construction workers.

목차

Abstract
1. 서론
1.1 연구배경 및 목적
1.2 연구범위 및 방법
2. 이론적 고찰
2.1 근골격계 질환 예방을 위한 위험 평가방법
2.2 족저압 데이터 기반 근골격계 부상 방지 기술
3. 족저압 데이터 분석 방법
3.1 데이터 수집 환경
3.2 데이터 전처리 및 분석
3.3 Bi-LSTM 및 성능평가 방법
4. 족저압 데이터를 활용한 신체부하 인식성능 결과
5. 결론
REFERENCES

키워드

저자

• 박근형 [ Park, Guenhyung | 전남대학교 건축학부, 공학석사 ]
• 고성석 [ Go, Seong-seok | 전남대학교 건축학부 정교수, 공학박사 ] Corresponding Author
• 황영서 [ Hwang, Youngseo | 전남대학교 건축학부, 공학석사 ]
• 양강혁 [ Yang, Kanghyeok | 전남대학교 건축학부 부교수, 공학박사 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  대한건축학회연합논문집 [Journal of the Regional Association of Architectural Institute of Korea]

• 간기

  격월간

• pISSN

  1229-5752

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 540 DDC 690