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1 Journal of the Korean Society of Safety, Vol. 28, No. 1, pp , February 2013 by The Korean Society of Safety (pissn ) All right reserved. 박동현 허국강 * 인하대학교산업공학과 * 인하공업전문대학항공경영과 ( 접수 / 채택 ) A Case Study on Risk Levels of Associated with Work-related Musculoskeletal Disorders at Automobile Manufacturing Industry Dong Hyun Park Kuk Kang Hur * Department of Industrial Engineering, Inha University * Department of Airline Service Management, Inha Technical College (Received October 17, 2012 / Accepted January 29, 2013) Abstract : This study tried to develop a basis for quantitative index of working postures associated with WMSDs(Work-related Musculoskeletal Disorders) that could overcome realistic restriction during application of typical checklists for WMSDs evaluation. The baseline data for this study was obtained from automobile manufacturing company(a total of 603 jobs were observed). Specifically, data for shoulder postures was analyzed to have a better and more objective method in terms of job relevance than typical methods such as OWAS, RULA, and REBA. Major statistical tools were Clustering, Logistic regression and so on. The main results in this study could be summarized as follows; 1) The relationships working postures and WMSDs s at shoulder were statistically significant based on the results from logistic regression. 2) Based on clustering analysis, three levels for WMSDs risk at shoulder were produced for both and abduction were statistically significant. Specific results were as follows; : low risk(< 37.7 ), medium risk(37.7 ~70.0 ), high risk(> 70.0 ) abduction: low risk(< 26.5 ), medium risk(26.5 ~56.8 ), high risk(> 56.8 ). 3) The sensitivities on risk levels of shoulder and abduction were 64.0% and 20.6% respectively while the specificities on risk levels of shoulder and abduction were 99.1% and 99.3% respectively. The results showed that the data associated with shoulder postures in this study could provide a good basis for job evaluation of WMSDs at shoulder. Specifically, this evaluation methodology was different from the methods usually used at WMSDs study since it tried to be based on direct job relevance from real working situation. Further evaluation for other body parts as well as shoulder would provide more stability and reliability in WMSDs evaluation study. Key Words : WMSDs, quantitative risk, shoulder postures, WMSDs, clustering, logistic regression 1. 서론 최근자료에의하면근골격계질환자는 2010 년의 5,502 명에대비하여 2011 년에는약 7.7% 감소한 5,077 명으로나타났다 1). 최근근골격계질환발생의이와같은지속적인감소는 2003 년에도입된근골격계질환예방의무제도가정착되고, 작업관련성질환예방을위한정부의지원사업이확대된결과라고판단된다 2). 그러나아직도일부사업장에서는직접적으로유해요인조사를실시하는데있어서그결과에대한해석이나방법자체의장단점을충분히이해하지못하고있는경우가많이있는것으로나타나고있다 3). 일반적으로사업장에서사용되고있는근골격계질환관 련유해요인조사를위한평가도구는 2003 년에처음으로노동부에서제시한 근골격계질환부담작업유해요인조사지침 (KOSHA Code H-30) 을필두로하여 OWAS(Ovako Working Posture Analysing System), RULA(Rapid Upper Limb Assessment) 나 REBA(Rapid Entire Body Assessment) 등을들수있다 4). 특히유해요인조사에가장빈번하게사용되는 OWAS, RULA, REBA 등은작업자세에기본하고있는데, 이러한근골격계질환과부적절한작업자세와의상관관계는이미많은연구결과에서보고되고있다 5-7). 그러나이평가도구들은경우에따라그적용에있어서몇가지제한점도가지고있다. 예를들어이러한도구들을사용하는데있어서대상작업의작업주기가극단적으로짧지않 Corresponding Author: Dong Hyun Park, Tel: , dhpark@inha.ac.kr Department of Industrial Engineering, Inha University, 253, Yong-Hyun Dong, Nam-Gu, Incheon , Korea 95

2 박동현 허국강 다면동일작업에대해서도평가자에따라서로다른작업자세를문제의자세로선택할수있다. 이경우최종평가결과는평가자에따라달라질수있으며, 그동안이와같은문제점들에대하여는많은지적이있어왔다 8). 그동안평가도구들의이와같은문제점을구체적으로분석하기위해많은연구가시도되었다. 여러제조업종에속한 224 개의작업에대해평가하였는데, 업종에상관없이 RULA 와 OWAS 는 33.5%, RULA 와 REBA 는 46.0%, OWAS 와 REBA 도약 54.0% 의일치율을보여평가결과가일치할확률이높지않는것으로나타났으며, 업종별 ( 철강산업, 자동차산업, 전자산업, 화학산업 ) 로분석한결과 OWAS 와 REBA 는 RULA 에비하여전반적으로작업자세부하를저평가하는것으로나타났다고보고하였다 9). 지각불편도와각도구 (OSHA, RULA, REBA) 를이용하여자동차조립공정의대표적인작업자세 42 개를평가한후에각도구간의결과, 각도구와지각불편도와의관계를비교하였다 10). 평가된부하수준들의값들간의상관관계를분석한결과, 세평가기법모두지각불편도와양의상관관계를보였으며, 상관계수는 REBA, OWAS, RULA 의순으로나타났으며, 전신의자세를평가함에있어서는 REBA 가가장적합한것으로나타났다. OWAS 와 REBA 의평가결과는높은상관관계를보였으나, REBA 와 RULA 의평가결과는상관관계의정도가약한것으로나타났다. 50 개의비디오기록을대상으로 2 인의평가자가동시에관찰한후평가자간재현성, 평가자내재현성을비교하였다 11). 그결과관찰자내재현성은 OWAS 가가장높았으며관찰자간재현성은 RULA 가가장높게나왔다. 또한분석도구들에따른조치수준의상관관계도비교하였는데 REBA 는 OWAS 와 RULA 와상관성이있으며상관계수는각각 r=0.58 (p<0.01), r=0.48(p<0.01) 이라고보고하였으며두명관찰자간의재현성은기존의연구결과 12,13) 보다상대적으로낮게나타났는데, 이는두평가자간의평가도구에대한숙련도차이로판단하였다 11). 따라서실제작업현장의작업들에대한주요평가도구의적용에는평가대상업종, 평가자의특성 ( 평가도구사용의숙련정도등 ) 등과관련되는현실적인어려움의가능성과그에대한해결대책도함께고려하여야한다고판단된다. 본연구에서는앞에서언급한유해요인조사에서흔히쓰이는주요작업관련성근골격계질환평가도구들의현실적인한계를극복하는데기초가될수있는작업자세의정량적위험도를도출하고자하였다. 일반적으로유해요인평가나작업의위험도를평가하는방법으로사용되고있는거의모든체크리스트형태의평가도구들은작업주기동안한두가지대표작업자세또는열악한작업자세한두동작에대한정보만을분석하여작업의위험도를평가하므로작업의강도나지속시간등을정확하게평가하기어렵다. 예를들어허리를굽히는작업자세가한번, 두번발생하는작업의정량적위험도와작업주기동안지속적으로허리굽힘동작이발생하는작업의정량적위험도는변별성이높아야되나실제로현재사용되는체크리스트로는그것을실현하기매우힘들다고판단되며이와같은경우 에는동일한작업에대하여분석결과에대한측정자간일치율을비교했을때차이가비교적크다는한계점이존재한다. 구체적으로본연구에서는자동차조립작업을대상으로하여전체작업주기동안작업자세관련위험도를작업자의근골격계질환관련증상을토대로정량적으로표현하여, 평가자나피평가자들이보다더현실감있는위험도예측을가능하게할수있는토대를마련하고자하였다 연구대상 2. 연구방법 본연구에서는 A 자동차회사의주요부서의총 603 개공정 ( 작업주기 : 약 0.9~3.0 분 ) 들을기본연구대상으로하고, A 사전작업자를대상으로한증상설문의결과를이용하여작업자세에대한정량적위험도를도출하고자하였다. 참고로설문에는주요신체부위에있어서의증상뿐만아니라현재수행하는작업, 작업경력, 병력등을포함하였다 작업자세측정 공정별작업자세분석은본연구에서개발한동작분석시스템을이용한미시 ( 微視 ) 동작분석 (Micromotion Study) 에그기본을두고있는데모든데이터는각신체부위별각도로표기된다. 시스템을사용하기에앞서서수행된비데오촬영은 Fig. 1 과같이상반신촬영을기본으로하였으며, 측면 ( 어깨굴곡분석용 ) 과정면 ( 어깨외전분석용 ; 현장상황이여의치못할경우에는후면 ) 에서각각 1 작업주기씩진행되었다. 동작분석시스템을이용한미시동작분석의과정은다음과같다. 1) 전체작업주기동안해당작업의작업자세를매 2 초마다연속된사진으로표현한다. 2) 변환된사진을동작분석시스템을이용하여분석한다. 이시스템과연결된사진위에는세개의점이나타나는데이것을신체역학 (Biomechanics) 에근거한작업자세의정의에따라분석자가각신체관절끝부분에위치시키면 ( 예를들어해당장면에서어깨굴곡각도를측정하기위해서는각점을손목, 어깨, 허리에위치시키면해당신체부위의각도를측정할수있다 (Fig. 1). Fig. 1. Measurement for shoulder (120 o ). 96 Journal of the KOSOS, Vol. 28, No. 1, 2013

3 Fig. 2. Variation of shoulder abduction angle. 3) 이시스템에서는측정된신체부위별각도의최대값, 최소값, 평균과표준편차에대한정보가그래프와함께결과로서출력되고, 출력된자료를이용하여작업자의신체부위별작업자세를분석한다 (Fig. 2) 군집화분석 (Cluster Analysis) 본연구는작업자세위험도결정의사례적접근방법으로군집화분석 (Cluster Analysis) 을활용하였다. 군집화분석 (Cluster Analysis) 이란자료뒤에숨어있는내재적그룹들을드러내는데쓰이는탐색적자료분석기법인데이분석에서는개체 (units, records) 간거리에근거하여, 서로가까운개체들은같은군집으로묶이고서로상당히떨어진개체들은다른군집에속하도록한다 14,15). 이상적으로는, 군집내 ( 內 ) 개체들은동질적이되고군집간 ( 間 ) 개체들은이질적이된다. 군집화에는여러방법이있지만, 본연구에서는자료의수와특징등을고려하여비계층적군집화 (Nonhierarchical Clustering) 의한방법인 K- 평균군집화방법을이용하여자료를분석하였으며, 군집중심은 Maxmin 방법으로구하였다 18). 본연구에서는각신체부위별로측정된각도에대한 K- 평균군집화방법을이용한범주화에있어서각군집간 ( 間 ) 의특징이뚜렷하게나타나는군집수를결정하기위하여최초군집의수를 3 개로지정하여군집을실시하였고, 군집간 ( 間 ) 차이가존재하지않으면군집의수를증가시켜다시군집화를하는방법을선택하였다. 또한군집에속한변수의수가적을때에는가장가까운군집에병합시켜군집을결정하였다. 본연구에서는전체 603 개공정중무작위로선택한 402 개공정 ( 전체자료의 2/3) 의자료를훈련 (Training) 데이터로사용하여군집분석을하였으며, 나머지 201 개공정 ( 전체자료의 1/3) 의자료를테스트 (Test) 데이터로사용하여군집화결과의타당성을검증하였다. 군집화관련분석은 SPSS 사의 Clementine(v.8.1) 을활용하였으며, ANOVA, Logistic 회귀분석등은 SPSSWIN (v.12.0) 을이용하여수행하였다. 3. 결과 3.1. 증상설문 증상설문자료의검증본연구에서는미리조사한증상설문조사결과와근골격계질환발생으로인한치료데이터를연계하여분석하였다. 다음그림은통증을경험하였다고응답한경우, 실제로해당신체부위에근골격계질환이발생한비율을표시하고있다 (Fig. 3). Fig. 3 의결과를토대로하여볼때, 본연구의증상설문에서자각통증이있다고응답한작업자는해당부위에실제로근골격계질환이발생할가능성이높다고판단된다. 특히, 어깨, 허리, 목의경우에는자각증상이실제근골격계질환의발생으로직접관련될확률이매우높은것으로나타났다. 본연구에서는증상과실제진단결과와의일치율이가장높은신체부위중어깨부위의작업자세를정량적위험도평가의주요대상으로다루었다. 근골격계질환에관련되는어깨자세에는어깨굴곡과어깨외전을들수있는데일반적으로굴곡 () 이라함은 해당관절을중심으로연결되는두개의분절이이루는관절각이감소하는움직임 을의미하고외전 (abduction) 이라함은 인체를좌우대칭으로나누는중심선으로부터인체분절이멀어져가는움직임 을의미한다. 구체적으로어깨굴곡과외전은 Fig. 4 와같다. Fig. 3. Percentage of accordance for the workers with s who were diagnosed with MSDs at corresponding body parts. Fig. 4. Types of working postures at shoulder 통증유무집단에따른작업자세측정각도의차이에대한검증증상설문을통하여작업자가응답한어깨부위에서의 통증있음 과 통증없음 집단간 ( 間 ) 의어깨작업자세측정각도의평균차이가통계적으로유의한지의여부를분산분석을통하여확인하였다 (Table 1,2). 그결과, 어깨굴곡및외전의평균각도는모두집단간에서유의한차이가있는것으로나타났다. 결과적으로, 본연구에서기초자료로사용하고있는각신체부위별로측정된각도의결과치는통증의유무에따른집단을결정하는중요한변수이며, 통계적으로통증의발생확률이높은작업을분류하는데영향력이높다고판단되었다. 한국안전학회지, 제 28 권제 1 호, 2013 년 97

4 박동현 허국강 Table 1. Descriptive statistics for shoulder angle with /no & Symptoms abduction N Mean SD lower 95% CI upper Min Max no total no total Table 2. ANOVA for shoulder postures of s with and no abduction Source of Variation Sum of Squares Degrees of Mean Square F total total ) 작업자세와통증유무에대한로지스틱회귀분석어깨작업자세와관련통증에대하여이항반응회귀분석 (binary response( 통증있음 / 없음 ) regression) 의한방법인이분형로지스틱회귀분석을이용하여어깨에서의작업자세와통증유무와의관계를살펴보았다 (Table 3). 그결과, 어깨에서의작업자세는모두 ( 굴곡, 외전 ) 통계적으로유의하였고, 따라서어깨에서의작업자세들 ( 굴곡, 외전 ) 은모두통증과관련성이있는것으로판단되었다. Table 3. Results of logistic regression for shoulder postures of s with and no B S.E. Wald Degrees of 95% CI for Exp(B) Exp(B) lower upper abduction Constant K-평균군집화 (K-means Clustering) 를이용한자료의범주화 K-평균군집화기법을이용하여각신체부위별로측정된각도를범주화하였다. 각군집간 ( 間 ) 의특징이뚜렷하게나타나는군집수를결정하기위하여최초군집의수를 3개로지정하여군집화를실시하였고, 군집간 ( 間 ) 차이가존재하지않으면군집의수를증가시켜다시군집 Table 4. Results from cluster analysis for shoulder Cluster 1(N=200) Cluster 2(N=132) Cluster 3(N=70) Mean(37.70) SD(7.79) Mean(13.26) SD(9.36) Mean(70.04) SD(13.20) Table 5. Cross tabulation of clusters and (shoulder ) Cluster 1 Cluster 2 Cluster 3 Clusters Symptom( ) No Yes Frequency % Frequency % Frequency % Table 6. Risk levels for shoulder angles based on cluster analysis Risk Levels Low Medium High angle(degree) < < > Number of cases with s Ratio of cases with s 22/242 (0.09) 46/132 (0.35) 27/28 (0.96) 화를하는방법을선택하였다. 또한군집에속한변수의수가적을때에는가장가까운군집에병합시켜군집을결정하였다 어깨굴곡각도의범주화앞서언급한바와같이최초에최소군집의수를 3 개로지정하여분석한결과, 군집수 3 개의경우가군집간 ( 間 ) 차이를가장잘표현하는군집분석의최종모델로나타났다. Table 4 는어깨굴곡각도에관한군집결과이고 Table 5 는각군집에있어서의통증관련정보이다. Table 6 은 Table 4 와 Table 5 의결과를토대로하여구성한어깨굴곡각도에대한정량적위험수준의정보이다 어깨외전각도의범주화어깨외전의경우에도어깨굴곡의경우와같이최초에최소군집의수를 3 개로지정하여분석한결과, 군집수 3 개의경우가군집간 ( 間 ) 차이를가장잘표현하는군집 Table 7. Results from cluster analysis for shoulder abduction Cluster 1(N=90) Cluster 2(N=39) Cluster 3(N=273) Mean(26.53) SD(8.01) Mean(56.75) SD(10.85) Mean(2.07) SD(4.28) Table 8. Cross tabulation of clusters and (shoulder abduction) Cluster 1 Cluster 2 Cluster 3 Clusters Symptom( abduction) No Yes Frequency % Frequency 3 36 % Frequency % Journal of the KOSOS, Vol. 28, No. 1, 2013

5 Table 9. Risk levels for shoulder abduction angles based on cluster analysis Risk Levels Low Medium High abduction angle(degree) < < > Number of cases with s Ratio of cases with s 48/319 (0.15) 32/67 (0.48) 15/16 (0.94) 분석의최종모델로나타났다. 즉군집의수를 3 개로지정하여어깨외전각도에대한군집분석을실시하였을때, 군집간 ( 間 ) 차이가가장정확하게나타났다. Table 7 은어깨외전각도에관한군집결과이고 Table 8 은각군집에있어서의통증관련정보이다. Table 9 는 Table 7 과 Table 8 의결과를토대로하여구성한어깨외전각도에대한정량적위험수준의정보이다 군집화결과의타당성검토 본연구에서는앞서도출된군집화결과의활용에앞서타당성을검토하였다. 구체적으로두가지방법을사용하여군집화결과에대한타당성을검토하였다 군집간 ( 間 ) 사후검정을위한분산분석 Table 10 과 Table 12 는어깨굴곡및외전각도에대하여 K-means 군집분석에대하여일원배치분산분석을실시한결과인데집단간어깨굴곡및외전각도모두에대하여유의한차이가있는것으로나타났다. Table 10. ANOVA for shoulder angles clusters Source of Variation Sum of Squares Degrees of Mean Square F total Table 11. Comparisons of shoulder angles among clusters based on scheffe's method (I) Cluster (J) Cluster Difference (I-J) Standard Error Lower 95% CI Upper * * * * * * Table 12. ANOVA for shoulder abduction angles clusters Abduction Source of Variation Sum of Squares Degrees of Mean Square F total Table 13. Comparisons of shoulder abduction angles among clusters based on scheffe's method (I) Cluster (J) Cluster Difference (I-J) Standard Error Lower 95% CI Upper * * * * * * 또한 Table 11 과 Table 13 은어깨굴곡및외전각도에대하여 Scheffe 를이용한사후검정 (Post Hoc Tests) 결과이다. 여기서, 어깨굴곡및외전각도모두는군집별로유의한차이가있는것으로나타났다 평가자료를이용한결과평가 K-means 군집결과를토대로하여만들어진위험도평가방법의타당성여부를증명하기위한두번째방법으로써조사된전체 603 개의공정중 2/3 인 402 개공정의자료를훈련 (Training) 자료로사용하여군집한결과를토대로나머지 1/3 인 201 개의자료 (Test Data) 를이용하여그타당도를검증하고자하였다. 이를위하여각신체부위별측정각도가군집결과에근거한저위험수준집단에속하면통증이없는것이라고간주하고, 고위험수준집단에속하면통증이있는것이라고간주하였다. 특히중위험수준은통증과비통증이혼재되어있는경계값 ( 통증자의비율 : 35% ( 어깨굴곡 ), 48%( 어깨외전 )) 이라고판단하여중위험수준집단에속하는값을가진작업자의통증유무는현단계에서의타당성평가에는포함시키지않았다 (Table 6,9). 각신체부위별통증유무판정결과는다음과같다. 결과표에서민감도의수치는통증이있는작업자를통증이있다고판정을내린결과의비율이고특이도의수치는통증이없는작업자를통증이없다고판정을내린결과의비율이다. Table 14 는어깨굴곡각도위험수준에의한통증유무판정결과이다. 결과에서볼수있듯이통증이있는작업자를통증이있다고판정을내린결과의정확도는 64.0% 이고통증이없는작업자를통증이없다고판정을내린결과의정확도는 99.1% 로나타났다. Table 15 는어깨외전각도위험수준에의한통증유무판정결과이다. 어깨외전의경우에는통증이있는작업자를통증이있다고판정을내린결과의정확도는 20.6% 이고통증이없는작업자를통증이없다고판정을내린결과의정확도는 99.3% 로나 Table 14. Test results for risk levels of shoulder angle Based on Risk Levels Symptoms at Accuracy(%) No Yes Actual Symptoms at No Yes *Sensitivity: 0.206, Specificity: 한국안전학회지, 제 28 권제 1 호, 2013 년 99

6 박동현 허국강 Table 15. Test results for risk levels of shoulder abduction angle Based on Risk Levels Symptoms at No Yes Accuracy(%) Actual Symptoms at No % Yes % *Sensitivity: 0.206, Specificity: 타났다. 4. 고찰및결론 현재많이활용되고있는작업자세평가도구를살펴보면, OWAS(Ovako Working-posture Analysis System) 의경우는광범위하게적용될수있는장점이있으나정밀한분석에는미흡한단점이있다. RULA 와 REBA 의경우에는상대적으로광범위한적용가능성에있어서는 OWAS 의경우보다한계를가질수있으나정밀한분석을하는데있어서, 특히상대적으로높은민감도를요구하는분석에는 OWAS 의경우보다강점을가진다고알려져있다 16). 일반적으로이와같은타당성평가는관찰자간및관찰자내의일치율에근거하여왔는데, 관찰자내일치율의경우, 85% 이상이면좋은일치도 (good agreement) 라고알려져있고 5), 관찰자간의일치율의경우, 75% 이상이면그결과를인정 (acceptable) 할수있다고알려져있다 15). 하지만이와같은타당성에대한고려만으로도출된작업위험도결과의객관성및정확성을설명하기에는많은한계가있다. 기존평가방법의이와같은한계를보완하기위해서는평가자의신뢰도및타당도를파악하기에앞서서해당작업을수행해온작업자의근골격계질환에대한증상경험여부와해당작업에서의작업자세정보와의관련성을규명하고, 그결과를적용하는것이한가지방법이될수있으리라판단된다. 따라서본연구에서는해당작업을수행해온작업자의근골격계질환에대한증상경험여부와해당작업에서의작업자세정보와의관련성을파악하기위하여근골격계질환위험도평가의사례적접근을평가방법화하고자하였다. 본연구의주요결과를정리하면다음과같다. 첫째, 어깨작업자세와관련통증에대하여이분형로지스틱회귀분석을이용하여어깨에서의작업자세와통증유무와의관계를살펴본결과, 어깨에서의작업자세는모두 ( 굴곡, 외전 ) 통계적으로유의한것으로나타났다. 따라서어깨에서의작업자세들 ( 굴곡, 외전 ) 은모두통증과관련성이있는것으로판단된다. 둘째, 군집분석결과, 저, 중, 고위험도의어깨자세범주는다음과같으며분산분석등의통계분석결과, 통계적으로유의한것으로나타났다. 어깨굴곡 ( 저 : < 37.7, 중 : 37.7 ~70.0, 고 : > 70.0 ) 어깨외전 ( 저 : < 26.5, 중 : 26.5 ~56.8, 고 : > 56.8 ) 본연구에서도출된저, 중, 고위험도의어깨자세위험도 는기존의방법과비교하여작업관련사례적접근이라는측면에있어서의미가있다고사료된다. 셋째, 조사된전체 603 개의공정중 2/3 인 402 개의자료를훈련 (Training) 자료로사용하여군집한결과를토대로나머지 1/3 의자료를이용하여테스트 (Test) 를한결과, 어깨굴곡의경우에는통증이있는작업자를통증이있다고판정을내린결과의정확도는 64.0% 이고, 통증이없는작업자를통증이없다고판정을내린결과의정확도는 99.1% 로나타났다. 어깨외전의경우에는통증이있는작업자를통증이있다고판정을내린결과의정확도는 20.6% 이고, 통증이없는작업자를통증이없다고판정을내린결과의정확도는 99.3% 로나타났다. 특히두작업자세 ( 어깨굴곡및외전 ) 모두에서민감도는특이도에비하여상대적으로낮은것으로나타났는데이런결과의주된원인은일반적으로고위험수준에포함되는자료의수가저위험수준에포함되는자료의수보다상대적으로적기때문이라고판단된다. 실제로어깨작업자세 ( 굴곡, 외전 ) 의경우고위험수준에작업자가상대적으로적게포함되어있었다. 현재우리나라의경우, 근골격계질환예방관리는인간공학적측면, 그중에서도작업관련성을고려함에있어서매우미흡한실정이다. 또한대부분의사업장은작업에관련된인간공학적요소들을축적한자료가체계적으로구축되어있지않고, 근골격계질환에대한객관적인평가기준또한미흡한실정이다. 본연구는이러한문제들을극복하고체계적인근골격계질환예방관리를위하여정량적평가기준의타당성을정립하는데그의미가있다고할수있다. 특히결과의타당성검증부분에서확인한바와같이본연구의결과는작업관련성등을고려한보다현실적이고체계적인평가방법을도출하는데있어서좋은토대가되리라판단된다. 앞으로본연구에서다룬어깨부위에더하여다른신체부위작업자세에대한정량적위험도의결정은작업관련성을고려한위험도평가를보다더향상시킬것으로사료된다. References 1) KOSHA, Industrial Accident Statistics, ) Department of Labor, Industrial Accident Facts, ) J. R. Kim, Ergonomics for Prevention of Work-related Musculoskeletal Disorders, Minyoungsa, ) KOSHA, KOSHA Code H-30, KOSHA, ) D. Altman, Practical Statistics for Medical Research, Chapman & Hall, London, ) T. Armstrong, P. Buckle, L. Fine, M. Harberg, B. Jonsson, A. Kilbom, L. Kuorinka, B. Silverstein, G. Sjogaard and E. Viikari-Juntura, A Conceptual Model for Work-related Neck and Upper-limb Musculoskeletal Disorders, Scandinavian Journal of Work, Environment and Health, Vol. 19, pp. 73~74, ) B. (Ed.), Bernard, Musculoskeletal Disorders and Workplace 100 Journal of the KOSOS, Vol. 28, No. 1, 2013

7 Factors: A Critical Review of Epidemiological Evidence for Work-related Musculoskeletal Disorders of the Neck, Upper Exterimity, and Lower Back. DHHS(NIOSH)Publication No. 97~141, US Department of Health and Human Services ) W. T. Kwak, A Study on the Variation in Action Levels of Various Workload Evaluation Tools, Hankyong National University The Graduate School of Technology, Master's Thesis, ) D. H. Kee and K. H. Park, Comparison of Posture Classification Schemes of OWAS, RULA and REBA, Journal of the Korean Society of Safety, Vol. 20, No. 2, pp. 127~132, ) I. S. Lee, M. K. Chung and K. I. Choi, Comparison of Observational Posture Evaluation Methods based on Perceived Discomfort, Journal of the Ergonomics Society of Korea, Vol. 22, No. 1, pp. 43~56, ) Y. C. Shin, J. T. Lee and D. M. Kang, Evaluation on Reliability and Validity of Rapid Entire Body Assessment, Korean Society of Occupational and Environmental Hygiene, Vol. 14, No. 1, pp. 1~6, ) I. Brujin, A. Engle and van der J. Gulden, A Simple Method to Evaluate the Reliability of OWAS Observations, Applied Ergonomics, Vol. 29, No. 4, pp. 281~283, ) M. Mattila, W. Karwowski and M. Vilkki, Analysis of Working in Hammering Tasks on Building Construction Sites using the Computerized OWAS Method, Applied Ergonomics, Vol. 24, No. 6, pp. 405~412, ) J. Jr. Hair, R. Anderson, R. Tatham, and W. Black, Multivariate Data Analysis with Reading, Prentice Hall ) R. Johnson and D. Wichern, Applied Multivariate Statistical Analysis, Prentice Hall ) S. Hignett and L. McAtamney, Rapid Entire Body Assessment(REBA)., Applied Ergonomics, Vol. 31, No. 2, pp. 201~ 205, ) G. Li and P. Buckle, A Practical Method for the Assessment of Work-related Musculoskeletal Risks - Quick Exposure Check (QEC), Proceedings of the Human Factors and Ergonomics Society 42nd Annual Meeting, pp. 1351~1355, ) C. Y. Jung and E. K. Choi, Statistical Analysis using SPS- SWIN, Muyok Publishing, 한국안전학회지, 제 28 권제 1 호, 2013 년 101

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