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Original Article
Development of algorithm for work intensity evaluation using excess overwork index of construction workers with real-time heart rate measurement device
Jae-young Park, Jung Hwan Lee, Mo-Yeol Kang, Tae-Won Jang, Hyoung-Ryoul Kim, Se-Yeong Kim, Jongin Lee
Ann Occup Environ Med 2023;35:e24.   Published online July 19, 2023
DOI: https://doi.org/10.35371/aoem.2023.35.e24
AbstractAbstract AbstractAbstract in Korean PDFSupplementary MaterialPubReaderePub
Background

The construction workers are vulnerable to fatigue due to high physical workload. This study aimed to investigate the relationship between overwork and heart rate in construction workers and propose a scheme to prevent overwork in advance.

Methods

We measured the heart rates of construction workers at a construction site of a residential and commercial complex in Seoul from August to October 2021 and develop an index that monitors overwork in real-time. A total of 66 Korean workers participated in the study, wearing real-time heart rate monitoring equipment. The relative heart rate (RHR) was calculated using the minimum and maximum heart rates, and the maximum acceptable working time (MAWT) was estimated using RHR to calculate the workload. The overwork index (OI) was defined as the cumulative workload evaluated with the MAWT. An appropriate scenario line (PSL) was set as an index that can be compared to the OI to evaluate the degree of overwork in real-time. The excess overwork index (EOI) was evaluated in real-time during work performance using the difference between the OI and the PSL. The EOI value was used to perform receiver operating characteristic (ROC) curve analysis to find the optimal cut-off value for classification of overwork state.

Results

Of the 60 participants analyzed, 28 (46.7%) were classified as the overwork group based on their RHR. ROC curve analysis showed that the EOI was a good predictor of overwork, with an area under the curve of 0.824. The optimal cut-off values ranged from 21.8% to 24.0% depending on the method used to determine the cut-off point.

Conclusion

The EOI showed promising results as a predictive tool to assess overwork in real-time using heart rate monitoring and calculation through MAWT. Further research is needed to assess physical workload accurately and determine cut-off values across industries.

실시간 심박수 측정 장치를 이용한 건설업 근로자 과로 예측 알고리즘
목적
건설업 근로자는 육체적 업무강도가 높아 이에 따른 피로와 그에 따른 사고 및 건강 악영향 우려가 높다. 이 연구에서는 일개 사업장 건설업 근로자를 대상으로 과로와 심박수 사이의 관계를 탐색하고 실시간 심박수 모니터링을 이용하여 과로를 방지하는 방법을 제안하였다.
방법
서울의 일개 건설업 현장에서 근무하는 근로자를 대상으로 2021.08.-2021.10.까지 근무 중 심박수를 측정하였다. 총 66명의 한국인 근로자가 손목시계 형 장치를 이용하여 심박수 측정에 참여하였다. 안정시 심박수와 최대 심박수 추정치를 바탕으로 상대심박수(RHR)를 산출하였고, 이를 바탕으로 피로 없이 근무할 수 있는 최대시간(MAWT)를 계산하여 이를 바탕으로 업무 부담(workload)를 추정하였다. 이를 통해 산출한 누적 업무량으로 과로 지수(Overwork index)를 산출한 후 적정 업무 부담(PSL)과의 차이인 초과 과로 지수(Excess overwork index)를 정의하였다. EOI 값을 이용해 일평균 상대심박수 30%값을 기준으로 정의한 과로군-비과로군을 예측할 수 있는 최적 절단값을 ROC 분석을 통해 산출하였다.
결과
근로자 60명의 심박수 측정값을 분석한 결과 28(46.7%)가 일평균 상대심박수를 기준으로 과로군으로 분류되었다. ROC 분석 결과 EOI값이 과로를 예측하는데 유용함을 확인하였고 AUC값은 0.824였다. 최적 절단 값은 최적화 방법에 따라 21.8%에서 24.0%로 나타났다.
결론
실시간 심박수 모니터링과 MAWT를 이용해 산출한 EOI값은 과로를 예측하는데 유용한 지표임을 확인하였다. 단, 건설업을 비롯하여 다른 육체적 부담이 큰 업종에서 최적 절단값을 찾기 위해서는 추가 연구가 필요하다.

Citations

Citations to this article as recorded by  
  • Development of productivity model of continuous miner operators working in hazardous underground mine environmental conditions
    Siddhartha Roy, Devi Prasad Mishra, Hemant Agrawal, Ram Madhab Bhattacharjee
    Measurement.2025; 239: 115516.     CrossRef
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  • 1 Web of Science
  • 1 Crossref
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Original Article
The Effect of Socio-Economic Factors on Occupational Injuries in Korea: A Time Series Analysis
Ye Won Bang, Hyoung June Im, Young Jun Kwon, Seong Sik Cho, Tae Kyung Lee, In Ki Yoon, Young Su Ju
Korean Journal of Occupational and Environmental Medicine 2011;23(4):397-406.   Published online December 31, 2011
DOI: https://doi.org/10.35371/kjoem.2011.23.4.397
AbstractAbstract PDF
OBJECTIVES
We performed a time series analysis in order to identify the relationship between the occupational injury rate and socio-economic factors, and through this predict the occupational injury occurrence rate.
METHODS
We reviewed 168 sets of monthly data. For the statistical analysis, we used the economic index data provided by Statics Korea and the occupational injury index provided by the Workers' Compensation & Welfare Service gathered from 1994 to 2007. We performed a correlation analysis to find relationship between the occupation injury rate and economic factors. Using the correlation analysis result, we used time series analysis for the data in order to find out the association between occupational injuries and socio-economic indicators. We performed time series analysis to find out association occupation injury rate with socio-economic factors. In addition we ran a prediction occupational injury rate for 2008 and compared the result to the actual value.
RESULTS
The factors associated with occupational injuries were the daily worker index (b=0.394, p<0.0001), the mechanical index (b=-0.023, p=0.0043), the manufacturing operation index (b=0.152, p<0.0001), the workers compensation coverage expansion (b=1.189, p=0.015), the IMF index (b=-2.05, p<0.0001), and the after IMF index (b=-1.565, p=0.01). The daily worker index, manufacturing operation index, and workers compensation coverage expansion had an effect that increased the occupational injury rate. Conversely, the mechanical index and IMF variable tended to decrease the occupational injury rate.
CONCLUSIONS
This study suggests that the daily worker index, manufacturing operation index, workers compensation coverage expansion, and IMF variables are related factors in regards to occupational injury.

Citations

Citations to this article as recorded by  
  • Business Cycle and Occupational Accidents in Korea
    Dong Koo Kim, Sunyoung Park
    Safety and Health at Work.2020; 11(3): 314.     CrossRef
  • The Prediction of Industrial Accident Rate in Korea: A Time Series Analysis
    Eunsuk Choi, Gyeong-Suk Jeon, Won Kee Lee, Young Sun Kim
    Korean Journal of Occupational Health Nursing.2016; 25(1): 65.     CrossRef
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Original Article
Validity of the Energy Expenditure Prediction Program to Evaluate Energy Expenditure During Work
Dong Mug Kang, Ji Hoon Woo, Jin Sook Jun, Yong Hwan Lee, Byung Mann Cho, Su Ill Lee
Korean Journal of Occupational and Environmental Medicine 2004;16(3):303-315.   Published online September 30, 2004
DOI: https://doi.org/10.35371/kjoem.2004.16.3.303
AbstractAbstract PDF
OBJECTIVES
The Energy Expenditure Prediction ProgramTM (EEPP) has been considered as a simple and quantitative method to evaluate physical work load. However, the adoption of EEPP directly to Korean workers is problematic because it was developed in a laboratory setting for Caucasians. Therefore, this study was conducted to validate EEPP for Korean workers.
METHODS
The study subjects consisted of 60 workers from two factories. Cycle ergometer test was conducted to calculate physical work capacity, and heart rate monitoring was conducted to check heart rate during work. After observing the task, energy expenditure was estimated by EEPP.
RESULTS
EEPP underestimated energy expenditure less than EEHR (energy expenditure checked by heart rate) did(p<0.0001). The factors effecting EEHR were EEPP and task type. After dividing the task into regular and irregular tasks, the irregular task had a larger difference between the values from the two methods. We provided task specific regression models between EEHR and EEPP.
CONCLUSIONS
Because EEPP underestimated energy expenditure, it needs to be adjusted before use with Korean workers. It is suggested that different adjusting equations are formulated for regular and irregular tasks. Further study to develop a specific energy estimation model appropriate for Koreans is needed to obtain more precise estimation.

Citations

Citations to this article as recorded by  
  • Estimation Model of Energy Expenditure of Working in a Clean Room for Manufacturing Embedded Needles by Ergonomic Programs
    Tae-Eun Chung
    Transactions of the Society of CAD/CAM Engineers.2016; 21(1): 69.     CrossRef
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  • 1 Crossref
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