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A new scoring system for the Rapid Entire Body Assessment (REBA) based on fuzzy sets and Bayesian networks
International Journal of Industrial Ergonomics 80 (2020) 103058
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A new scoring system for the Rapid Entire Body Assessment (REBA) based on fuzzy sets and Bayesian networks Fakhradin Ghasemi a, *, Neda Mahdavi b a
Department of Ergonomics, Center of Excellence for Occupational Health, Occupational Health and Safety Research Center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran b Department of Ergonomics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
A R T I C L E I N F O
A B S T R A C T
Keywords: Risk assessment Bayesian network Work-related musculoskeletal disorders
Traditional methods for assessing the risk of work-related musculoskeletal disorders (WMSDs) have a low sensitivity to changes in input variables. Using them, it is possible to obtain the same risk score for totally different postures, and in some cases, the effectiveness of ergonomic interventions cannot be demonstrated. This study aimed to develop a new scoring system for REBA, FBnREBA, using fuzzy sets and the Bayesian network (BN) approach to cover the drawbacks of the traditional REBA. First, the risk factors of WMSDs were defined in terms of fuzzy membership sets. Next, a BN model was developed based on REBA. Fourteen different postures were assessed using FBnREBA, and the results were compared with those of the original REBA. Lastly, a case study was performed to demonstrate how the new scoring system can be used to rank various interventions based on their effectiveness. FBnREBA is a BN model with 26 nodes and is based completely on REBA, but its results differ from those of REBA for identical postures. A comparison of the results of FBnREBA with those of REBA indicated that FBnREBA is more sensitive to changes in WMSDs risk factors than REBA. A case study was con ducted using FBnREBA, and the effectiveness of modifying each body segment was determined and ranked. FBnREBA is more sensitive to changes in input variables so that it is unlikely to obtain the same risk score for different body postures. The introduced methodology can be used to modify the scoring systems of other similar methods.
1. Introduction 1.1. Methods for assessing the risk of work-related musculoskeletal disorders Work-related musculoskeletal disorders (WMSDs) are a major problem for both employers and employees in occupational settings and should be managed properly (Morse et al., 1998; Stewart et al., 2003). Risk assessment is the main tool for identifying jobs or tasks which are more prone to WMSDs. Using risk assessment, the level of exposure to various WMSDs risk factors is determined and improvement in terventions are prioritized. Methods and techniques for assessing the risk of WMSDs have been categorized into four main groups: pen and paper observational methods, videotaping and computer-assisted analyses, direct or instru mental techniques, and self-report assessment tools (Li and Buckle,
1999). Pen and paper observational methods are most popular, because they are easy to learn, easy to implement, inexpensive, and less time-consuming. OWAS (Ovako Working Analysis System) (Karhu et al., 1977), RULA (Rapid Upper Limb Assessment) (McAtamney and Corlett, 1993), and REBA (Rapid Entire Body Assessment) (Hignett and McA tamney, 2000) are among the most popular pen and paper observational methods. 1.2. Drawbacks of traditional methods Although these methods are very useful in providing deep insight into the risk of WMSDs, they have several drawbacks. One main draw back of traditional methods such as REBA is their low sensitivity to input variables. For example, with traditional methods, different postures may result in the same risk score, even though they impose different biomechanical effects on the body. For example, according to REBA,
* Corresponding author. Hamadan University of Medical Sciences, School of Public Health, Ergonomics Department, 65178-3-8736, Shahid Fahmideh BLVD, Hamadan, Hamadan province, Iran. E-mail address: [email protected] (F. Ghasemi). https://doi.org/10.1016/j.ergon.2020.103058 Received 13 November 2018; Received in revised form 22 February 2020; Accepted 30 October 2020 Available online 10 November 2020 0169-8141/© 2020 Elsevier B.V. All rights reserved.
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
there is no difference between trunk scores at 21◦ flexion and 59◦ flexion, even though the biomechanical stresses imposed on the body at these two postures are totally different. In contrast, when the upper arm position is at 44◦ flexion, the risk score is 2, but when it is at 46◦ flexion, the risk score is 3, which may result in different final REBA scores, even though the difference between these two postures does not seem to be significant. The scores associated with load/force suffer from the same problem. For example, there is no difference between a 12-lb load and a 22-lb load (both of them receive a score of 1), while the biomechanical stress they impose on the body can be totally different. This drawback has been mentioned in several previous studies (David, 2005; Golabchi et al., 2016; Madani and Dababneh, 2016) and may diminish the inter-observer reliability of traditional methods, particularly when the position of a body segment is close to the boundary between two ranges. The low inter-observer reliability of REBA, RULA, and other traditional methods have been mentioned by several studies, including Schwartz et al. (2019), Dockrell et al. (2012), and David (2005). In confirmation of these issues, McAtamney and Corlett (1993) explained that when the position of a body segment is close to the boundaries of ranges, some discrepancies may be observed in the result of risk assessment. Low sensitivity to input variables makes it difficult to demonstrate the effectiveness of ergonomic interventions. For example, assume an intervention modifies the trunk position by altering its flexion angle from 59◦ to 20◦ . This improvement has no effect on the final RULA or REBA score. This problem has also been mentioned in previous studies, such as Choobineh et al. (2004) and Sanchez-Lite et al. (2013). Simi larly, Li and Buckle (1999) stated that an acceptable ergonomic assess ment tool should be able to demonstrate the effectiveness of an ergonomic intervention. Accordingly, several studies have been conducted to develop new methods for covering these drawbacks. For example, Sanchez-Lite et al. (2013) developed the Novel Ergonomic Postural Assessment Method (NERPA) as an alternative of RULA. Golabchi et al. (2016) recom mended the use of fuzzy set theory to deal with such deficiencies and developed a fuzzy logic model based on RULA for assessing the ergo nomic risks in manual construction operations. They showed their method to be more accurate and able to reduce the probability of human error during assessment. Rivero et al. (2016) built a Fuzzy Inference System (FIS) based on RULA for WMSDs risk assessment. Pavlovic-Ve selinovic et al. (2016) developed the fuzzy expert system known as SONEX to predict WMSDs and suggest preventive interventions. In another study, Nunes (2009) developed the fuzzy expert system FAST ERGO X for auditing, assessing, and controlling exposure to ergonomic risk factors in workplaces. In the same vein, Ahn et al. (2018) con structed a diagnostic model of WMSDs useful in assessing the effects of various working characteristics such as working hours and work pace on WMSDs. They also reported that BN was stronger than artificial neural network, support vector machine, and decision tree approaches in assessing risk factors and predicting WMSDs.
body parts as well as all well-known WMSDs risk factors, i.e. awkward posture, force, and types of activity (repetitive and static). This method provides a better definition for neutral postures than RULA (Madani and Dababneh, 2016) and is appropriate for both static and dynamic tasks (Hashim et al., 2012). The intra- and inter-observer reliability of REBA has been demonstrated to be satisfactory (Hignett and McAtamney, 2000; Kee and Karwowski, 2007). However, similar to most ergonomic assessment tools, the predictive and concurrent validity of this method needs to be further elucidated in future studies (Madani and Dababneh, 2016). Since its invention, the method has been utilized extensively for ergonomic assessment in various sectors, including oil industries (Motamedzade et al., 2011), hospital nursing (Ratzon et al., 2016), dentistry (Jahanimoghadam et al., 2018), mining industries (Norhi ˘lu, dayah et al., 2016), forestry timber harvesting (Enez and Nalbantog 2019), sewing machine operation (Sakthi Nagaraj et al., 2019), and other sectors (Chiasson et al., 2012). A recent study showed that the use of this method is increasing among ergonomists (Lowe et al., 2019). It should be emphasized that the new methodology can also be used to modify the scoring system of other methods such as RULA. REBA equipped with the new scoring system will be called FBnREBA for the rest of the study. 2. Materials and methods 2.1. REBA method This method, first introduced by Hignett and McAtamney (2000), divides the body into two main parts; the first part is composed of the neck, trunk, and legs. Their scores are combined using Table A in the REBA worksheet to obtain a single value. The second part is composed of upper arm, lower arm, and wrist, and their scores are aggregated using Table B in the REBA worksheet. After adding the scores associated with coupling and force, the scores of these tables are combined using Table C. Lastly, the score associated with the type of activity is added. The final REBA score has a range from one to greater than eleven; the higher the final score is, the greater the risk of WMSDs will be. These scores and associated action levels are presented in Table 1. 2.2. Fuzzy set theory Fuzzy set theory, first introduced by Zadeh (1965), is a soft computing technique that is very helpful in dealing with the vagueness, partial truth, and uncertainty present in solving a problem (Gupta and Kulkarni, 2013). According to this theory, a single value can belong to several sets with different degrees of membership. Degree of member ship can adopt any value within the range of zero to one, which is against the classical view of sets in which the degree of membership of a variable to a set can only be either 0 or 1 (Jamshidi et al., 2013; Klir and Yuan, 1996). In the present study, fuzzy set theory was used to deal with the sudden changes in angle ranges of a body segment. Using this theory, the transition from one range to the next was gradual, which modified the traditional way of scoring input variables. For example, assume that the angle ranges of an upper arm position are defined using fuzzy mem bership functions as demonstrated in Fig. 1, and the angle of the upper arm in a specific body posture is at 18◦ flexion. When using traditional
1.3. The present study The low sensitivity of traditional methods to changes in input vari ables stems from the fact that they commonly follow the principles of classical (crisp) set theory. A method developed based on these princi ples would lead to sharp boundaries between the angle ranges of a body segment. Consequently, the final risk score suffers from low sensitivity to changes in input variables, thereby leading to inaccurate results in some cases. This drawback can be properly handled by fuzzy set theory (Zadeh, 1965) and Bayesian networks (BNs) (Pearl, 2014). Therefore, the main aim of the present study was to develop a new scoring methodology based on fuzzy set theory and BNs to modify pre vious methods so as to increase their sensitivity to input variables. REBA was selected to demonstrate how the methodology should be implemented. This method was selected because it is general in nature and applicable to a wide range of occupations. Moreover, it considers all
Table 1 The final risk score in the REBA method and associated action levels.
2
REBA score
Action level
1 2–3 4–7 8–10 >11
No action required Modification may be needed Further investigation may be needed Modification is required Modification is required immediately
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
Fig. 1. The process of translating the angle ranges of traditional REBA into fuzzy membership functions.
Fig. 2. The process of constructing FBnREBA based on REBA. 3
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
REBA, which is based on the crisp set theory, the score associated with this body segment would be 1. In contrast, when the fuzzy set theory is used, this angle of flexion belongs to two sets and consequently adopts two scores with different degrees of membership; its scores are 1 and 2 with 0.1 and 0.9 degrees of membership, respectively. In the present study, all input variables of REBA were translated into fuzzy membership functions using the process known as “fuzzification.” Triangular fuzzy membership sets were used in this step. There are other types of fuzzy membership functions to be used; however, the calcula tions associated with triangular membership sets for determining membership degrees are simpler than those of other fuzzy membership functions.
It should be mentioned that GENIE software developed by Bayes Fusion (www.bayesfusion.com) was used to perform all modeling and analyses related to BN. 2.4. Calculating the final risk score of WMSDs based on FBnREBA When the original REBA method is used, the final risk score is a single integer value in the range of 1–13. When FBnREBA is used, however, the result is a set of integer values with different probabilities. To transform them into a single value, their weighted sum should be calculated using the following equation: ∑ RS = Pi Si
2.3. Bayesian networks (BNs)
where RS stands for the final risk score, Pi is the probability associated with state i, and Si is the score associated with state i.
When the input variables of REBA or any similar method are expressed in terms of fuzzy membership functions, tables presented in the REBA or RULA worksheets (such as Tables A, B, and C in the REBA worksheet) can no longer be used for merging the scores of various body segments. Instead, studies have generally used fuzzy inference system (FIS), which contains a set of rules that create relationships between input variables and the output variable (Jamshidi et al., 2013). This property of fuzzy set theory was not used in the present study, because it needs a high number of rules which are tedious for both determining and inserting into the inference engine. The BN approach was used in this study to determine the relationships between the input variables and the risk of WMSDs. BNs are graphical analytical tools which have been used extensively in modeling and assessing risk in complex systems (Jensen and Nielsen, 2009). A BN is composed of a set of nodes representing the variables and a set of directed arcs depicting the causal relationships among the nodes (Ghasemi et al., 2017; Jensen and Nielsen, 2009). Each BN has a quantitative part known as a conditional probability table (CPT) that reflects how a variable is affected by its parents. In the other words, CPTs determine how a variable would change if its parents change (Jensen and Nielsen, 2009). To benefit from this modeling approach, the qualitative part, i.e. nodes and directed arcs, and quantitative part, i.e. CPTs, of the model must be determined. In this study, both of these parts were determined using the information extracted from the original REBA. To determine the structure of the BN, all input variables and the A, B, and C tables in the REBA worksheet were regarded as the nodes constituting the BN. As the score of each body segment was determined using its fuzzified angle ranges and the required adjustments, several new nodes related to the posture of various body segments and the required adjustments were also added. This process for determining a trunk score is depicted in Fig. 1, part A. For merging the risk scores of the neck, trunk, and legs, Table A from the REBA worksheet was regarded as a node in the BN model (see Fig. 2, part B). Likewise, for merging the risk scores of upper arm, lower arm, and wrist, Table B from the REBA worksheet was regarded as a new node and inserted in the BN model. To determine the CPTs of nodes representing the tables from the REBA worksheet, the same principles as the original REBA were used. For example, Table A in the REBA worksheet contains values ranging from 1 to 9, so the “score A′′ node in the BN model has nine states demonstrated by s1, s2, s3 … and s9. For assigning the CPT of this node, the same approach as Table A was adopted. For example, assume that a configuration of states of the neck, trunk, and leg positions corresponds to score 5 in Table A (based on the REBA worksheet). To determine the CPT of the “score A′′ node for this configuration, all states of “score A′′ were given a probability of zero, while state s5 received a probability one. An example of this process is presented in Fig. 2 (part C). In this figure, the CPT of Table B was built in the BN. Because the CPT of this node was too big in size to be presented, just that part which corresponds to that part of Table B, discriminated by a red rectangle, is presented (Fig. 2, part C).
2.5. Comparison of REBA and FBnREBA In this step, several postures were analyzed using both REBA and FBnREBA, and the differences were investigated. 2.6. Determining the most effective intervention strategy using BN reasoning One of the main characteristics of a BN is its ability to perform various types of reasoning, such as predictive, diagnostic, inter-causal, and combined reasoning (Abolbashari et al., 2018). The most effective way to reduce the risk of WMSDs can be determined using predictive reasoning. Accordingly, a real case was investigated, and the risk of WMSDs was determined using FBnREBA. Next, predictive reasoning was employed to determine the effectiveness of various interventions in reducing the final FBnREBA score. The case study was conducted in a desktop gas cooker assembly line. The line suffers from poor ergonomic conditions, and we wanted to know which intervention strategy was more beneficial than the others. Several postures were selected, and the worst one was analyzed using FBnREBA. Then, the intervention options were compared based on their effectiveness in reducing the risk of WMSDs. 3. Results 3.1. Fuzzification of input variables Figs. 3 and 4 demonstrate the fuzzy membership functions associated with various input variables of REBA. It should be stressed that the fuzzification process was not possible for some variables such as the position of the legs, coupling score, and activity score; thus, they were treated the same as the original REBA. Moreover, each fuzzy membership set can be expressed using a set of equations. Equations (1)–(5) are associated with upper arm angles. ⎧ ⎫ 1 x > 90 ⎨ ⎬ f (x)f 4 = (x − 50)/40 45 ≤ x ≤ 90 (1) ⎩ ⎭ 0 x < 45 ⎧ ⎪ ⎪ ⎨
⎫ 0 x > 90 ⎪ ⎪ ⎬ (90 − x)/45 45 < x ≤ 90 f (x)f 3 = 20 < x ≤ 45 (x − 20)/45 ⎪ ⎪ ⎪ ⎪ ⎩ ⎭ 0 x < 20 ⎧ ⎪ ⎪ ⎨
⎫ 0 x > 20 ⎪ ⎪ ⎬ (45 − x)/25 20 < x ≤ 45 f (x)f 2 = (x + 20)/20 0 < x ≤ 20 ⎪ ⎪ ⎪ ⎪ ⎩ ⎭ 0 x<0
4
(2)
(3)
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
Fig. 3. The fuzzy membership sets associated with force and positions of neck and trunk.
⎧ ⎪ ⎪ ⎨
⎫ 0 x > 20 ⎪ ⎪ ⎬ (20 − x)/20 0 < x ≤ 20 f (x)f 1 = (x + 20)/20 0 < x ≤ − 20 ⎪ ⎪ ⎪ ⎪ ⎩ ⎭ 0 x < − 20 ⎧ ⎨
⎫ 0 x>0 ⎬ f (x)e = (− x)/20 0 ≤ x ≤ − 20 ⎩ ⎭ 1 x < − 20
3. Trunk angle: This node represents the angle of trunk flexion. It has five states: e, f1, f2, f3, and f4. These states are associated with the fuzzy membership sets depicted in Fig. 3. 4. Trunk adjustment: This node represents any adjustment required for assigning the trunk score, including trunk twisting and side bending. This node has two states: no and yes. State no indicates that there is no need for adjustment; state yes indicates that adjustment is required. 5. Legs position score: This node represents the score of the legs, which can range from 1 to 4. The node has four states: s1, s2, s3, and s4 corresponding to scores 1, 2, 3, and 4, respectively. 6. Force: This node represents the amount of force required for performing a task. It has three states: s1, s2, and s3. The fuzzy membership functions associated with these states are presented in Fig. 3. 7. Force adjustment: This node is used to make any adjustment required for assigning the score of force, such as shock or rapid buildup of force. This node has two states, yes and no, explaining the need for adjustment. 8. Coupling score: This node demonstrates the score of coupling. The node has four states, s0, s1, s2, and s3, corresponding to scores of 0, 1, 2, and 3, respectively.
(4)
(5)
3.2. BN model constructed based on REBA method The BN constructed based on the REBA method had a total number of 26 nodes. A brief description of each node is presented below: 1. Neck angle: This node represents the angle of the neck flexion. It has three states: e, f1, and f2. These states are associated with the fuzzy membership sets depicted in Fig. 3. 2. Neck adjustment: This node represents any adjustment required for assigning the neck score, which includes neck twisting and side bending. It has two states: no and yes. State no indicates that there is no need for adjustment; state yes indicates that adjust ment is required. 5
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
Fig. 4. The fuzzy membership sets associated with positions of upper arm, lower arm, and wrist.
9. Upper arm angle: This node demonstrates upper arm angle flexion. The node has five states: e, f1, f2, f3, and f4. The fuzzy membership functions associated with these states are presented in Fig. 4. 10 . Upper arm adjustment: This node is used to insert the effect of any adjustment associated with the upper arm position on the upper arm score. The node has four states, s_1, s0, s1, and s2, corresponding to scores − 1, 0, 1, and 2. 11. Lower arm angle: This node demonstrates the lower arm angle and has three states: h_e, n, h_f. The fuzzy membership functions associated with this node are presented in Fig. 4. 12. Wrist angle: This node demonstrates the wrist angle and has three states: h_e, n, and h_f. The fuzzy membership functions associated with these states are presented in Fig. 4. 13. Wrist adjustment: This node demonstrates any adjustment required for assigning the score of the wrist. These adjustments are required when the wrist is twisted or is deviated from the midline. The node has two states, yes and no, demonstrating the need for any adjustment. 14. Trunk score: This node demonstrates the final score associated with trunk posture, which should be determined by considering the states of its parent nodes, i.e. “trunk angle” and “trunk adjustment.” The node has five states, s1, s2, s3, s4, and s5,
15.
16.
17.
18. 19.
6
corresponding to scores 1, 2, 3, 4, and 5, respectively. The CPT of this node is presented in Fig. 5. Neck score: This node represents the final score of the neck and is determined by its angle and required adjustment. The node has three states, s1, s2, and s3, corresponding to the scores of 1, 2, and 3. The CPT of this node is presented in Fig. 5. Force/load score: This node demonstrates the final score of force which is affected by its amount and required adjustment. The node has four states, s0, s1, s2, and s3, corresponding to scores 0, 1, 2, and 3. The CPT of this node is presented in Fig. 5. Upper arm score: This node demonstrates the final score of upper arm and is determined by its parent nodes, i.e. “upper arm angle” and “upper arm adjustment.” It also has six states, s1, s2, s3, s4, s5, and s6, corresponding to scores 1, 2, 3, 4, 5, and 6, respectively. Lower arm score: This node demonstrates the final score of the lower arm and is determined by its angle. The node has only two states, s1 and s2, corresponding to scores 1 and 2, respectively. Wrist score: This node demonstrates the final score of the wrist and is determined by its angle and required adjustments. The node has three states, s1, s2, and s3, corresponding to scores 1, 2, and 3, respectively. The CPT of this node is presented in Fig. 5.
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
Fig. 5. CPTs of several nodes of the BN model constructed based on the REBA.
Fig. 6. The BN model constructed based on the REBA method. 7
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
20. Score A: This node represents Table A from the REBA worksheet and is used to integrate the scores of the neck, trunk, and legs. It has nine states, s1, s2, s3 … s9, corresponding to scores 1, 2, 3 … 9, respectively. 21. Adjusted score A: This node is created to integrate the score ob tained from Table A with that of force/load. It has 12 states, s1, s2, s3 … s12, corresponding to scores 1, 2, 3 … 12, respectively. 22. Score B: This node represents Table B from the REBA worksheet and is used to integrate the scores of the upper arm, lower arm, and wrist. The node has nine states, s1, s2, s3 … s9, corre sponding to scores 1, 2, 3 … 9, respectively. 23. Adjusted score B: This node is created to integrate the scores of Table B and the coupling type. Similar to the “adjusted score A′′ node, this node has 12 states, s1, s2, s3 … s12, corresponding to scores 1, 2, 3 … 12, respectively. 24. Table C score: This node represents Table C from the REBA worksheet and is used to integrate the scores of the two nodes of “adjusted score A′′ and “adjusted score B". This node has 12 states, s1, s2, s3 … s12, corresponding to scores 1, 2, 3 … 12, respectively. 25. Activity score: This node demonstrates activity type and has two states, s0 and s1, corresponding to scores 0 and 1, respectively. When the activity is repetitive or static, the state of the node is s1; otherwise, it is s0. 26. WMSDs risk: This node demonstrates the final risk of WMSDs. It has 13 states, s1, s2, s3 … s13, corresponding to scores 1, 2, 3 … 13.
• Comparison of Posture II and Posture III: These two postures are also similar in all body regions except the position of the neck. The angles of the neck flexion in Posture II and Posture III are 10◦ and 19◦ , respectively. In contrast to REBA which provides the same risk score for these two postures, FBnREBA discriminates between them by providing two different risk scores: 4.81 for Posture II, and 6.74 for Posture III. • Comparison of Posture III and Posture IV: The only difference be tween these two postures is the angle of the trunk flexion, which is 2◦ higher in Posture IV than in Posture III. It is clear that the biome chanical stress of Posture III is slightly greater than that of Posture IV. The REBA method is unable to demonstrate this gentle difference, whereas FBnREBA differentiates between them by providing slightly different risk scores. • Comparison of Posture IV and Posture V: The only difference be tween these two postures is the angle of the trunk flexion, which is 21◦ for Posture IV and 59◦ for Posture V. Similar to previous pair comparisons, REBA cannot discriminate between these two postures, whereas FBnREBA provides different risk scores for them. • Comparison of Posture V and Posture VI: The only difference be tween these two postures is the angle of the neck flexion, which is slightly higher in Posture VI. REBA demonstrates this difference by providing a huge difference in risk scores for the two postures. An increase of 2◦ in the angle of the neck flexion increases the REBA score from 5 to 7, whereas FBnREBA treats this slight change in a more rational manner such that the risk scores for these postures are very close together. • Comparison of Posture VII and Posture VIII: The difference between these two postures is an increase of 2◦ in the angle of the trunk flexion. REBA reacts to this change by increasing the risk score from 7 to 8; however, there is a slight difference between risk scores provided by FBnREBA, which is more rational. • Comparison of Posture XIII and Posture XIV: What stands out in this comparison is the effect of force/load on the risk scores provided by REBA and FBnREBA. When the amount of force increases from 1 lb to 10 lbs, there is no change in the risk scores provided by REBA, while the risk score provided by FBnREBA increases from 10.1 to 10.9.
By implementing the above-mentioned methods, the BN model demonstrated in Fig. 6 was obtained. 3.3. Comparison of REBA and FBnREBA For comparison purposes, 14 different hypothetical postures (Table 2) were made and assessed using both REBA and FBnREBA. The two leftmost columns of this table demonstrate the results of these as sessments. To demonstrate the differences between these two methods, the following comparisons were made:
From the above comparisons, it can be inferred that FBnREBA is more sensitive to changes in input variables. The trend of changes in the risk scores obtained from these two methods is demonstrated in Fig. 7. As can be seen, the trends are ascending in both methods. However, the final REBA score increases in a stepwise manner, whereas the final FBnREBA score increases with a steady slope.
• Comparison of Posture I and Posture II: These two postures are similar to each other in all body segments except the trunk position. In Posture I, the trunk angle is at 1◦ flexion, while in Posture II, the trunk angle is at 19◦ flexion. These two postures exert different biomechanical effects on the body. 3D modeling using CATIA soft ware package showed that the L4/L5 compression forces for these two angles are 580N and 1384N, respectively. Although REBA is unable to discriminate between the risk scores of these two postures, FBnREBA provides two different risk scores for them.
3.4. Case study A case study was conducted in a desktop gas cooker assembly line.
Table 2 Characteristics of fourteen postures used for comparing FBnREBA and REBA. Posture
Neck angle
Trunk angle
Legs score
Force
Upper arm angle
Lower arm angle
Wrist position
adjustments
REBA score
FBnREBA
I II III IV V VI VII VIII IX X XI XII XIII XIV
10◦ 10◦ 19◦ 19◦ 19◦ 21◦ 21◦ 21◦ 21◦ 21◦ 21◦ 21◦ 21◦ 21◦
1◦ 19◦ 19◦ 21◦ 59◦ 59◦ 59◦ 61◦ 61◦ 61◦ 61◦ 61◦ 61◦ 61◦
2 2 2 2 2 2 2 2 2 2 2 2 2 2
1 lb 1 lb 1 lb 1 lb 1 lb 1 lb 1 lb 1 lb 1 lb 1 lb 1 lb 1 lb 1 lb 10 lb
5◦ 5◦ 5◦ 5◦ 5◦ 5◦ 40◦ 40◦ 40◦ 40◦ 40◦ 40◦ 46◦ 46◦
80◦ 80◦ 80◦ 80◦ 80◦ 80◦ 80◦ 80◦ 99◦ 101◦ 101◦ 101◦ 101◦ 101◦
0◦ 0◦ 0◦ 0◦ 0◦ 0◦ 0◦ 0◦ 0◦ 0◦ 14◦ 16◦ 16◦ 16◦
Trunk Trunk Trunk Trunk Trunk Trunk Trunk Trunk Trunk Trunk Trunk Trunk Trunk Trunk
5 5 5 5 5 7 7 8 8 8 8 9 10 10
4.19 4.81 6.74 6.93 7.98 7.99 8.87 8.90 9.28 9.32 9.78 9.80 10.1 10.9
8
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
The FBnREBA score for the posture was 7.63, while the REBA score was 6. In the next step, the effects of modification of various body seg ments on the final risk score of REBA and FBnREBA was investigated, and the results are presented in Table 3. All modifications could reduce the final FBnREBA risk score to some extent. In contrast, REBA was unable to demonstrate the effects of modifications on the neck and the lower arm postures (the prior and posterior risk scores were equal). Moreover, in contrast to REBA, FBnREBA was able to differentiate the positive effects of modifications on the upper arm and trunk postures. Furthermore, as FBnREBA is totally software-based, it is easier and less time-consuming to use in assessing the effects of modifications on the final risk score. 4. Discussion The present study aimed to develop a new scoring system for REBA such that the final risk score would become more sensitive to the posi tion of body segments. Fuzzy sets and Bayesian network approaches were used in developing the new system. Previous studies have only used fuzzy set theory to solve such a deficiency in traditional methods. The reason behind the use of fuzzy set theory is obvious: to avoid the sharp boundaries between angle ranges of a body segment, which lead to sudden changes in the scoring of input variables. Traditional methods are based on principles of classical (crisp) set theory, and fuzzy set theory is able to cover the deficiencies of these principles. Today, the application of fuzzy set theory instead of classical methods is commonplace in other areas of research and practice. For example, Jamshidi et al. (2013) used it to modify the Kent-Muhlbauer method of pipeline risk assessment, and Kutlu and Ekmekçioglu (2012) employed the theory to apply soft computing principles in modifying failure modes and the effects analysis (FMEA) method. The most prominent difference between the present study and pre vious ones is that most previous studies utilized FIS to relate the input variables to the output variable. To develop FIS, several steps should be carried out, including fuzzification of inputs, development of the rule base (a set of If-Then rules), determination of the aggregation method, and selection of the defuzzification method (Jamshidi et al., 2013). There are several ways to perform each of these steps, and each way may lead to a different output. Interestingly, there is no universally-accepted criterion for selecting the most appropriate way to perform each step (Yazdi and Zarei, 2018). Furthermore, the rule base, as the heart of FIS, is commonly developed based on the opinions of domain experts. This approach has some potential drawbacks which can affect the final re sults. For example, how many experts should participate in developing the rule base? What is the best approach to integrating the knowledge of experts? Who can be considered an expert in the area of interest? Therefore, because of the above-mentioned issues, the modification of traditional methods using FIS may cause deviation from the principles used in the original method. When BN is used instead of FIS, none of the above-mentioned obstacles will be faced, because the CPTs of the BN are developed based on the tables presented in the original method. In other words, the tables used in the REBA worksheet for merging the scores of various parts of the body are exactly mapped in the CPTs of the BN model constructed based on REBA. The methodology presented in this study is unique and, to the best of the authors’ knowledge, this paper is the first to have utilized BN to improve classical methods. Using BN to assess the risk of exposure to ergonomic risk factors has several advantages. BN is very flexible and widely used to model complex systems based on their causal relation ships (Akbari et al., 2018; Ghasemi et al., 2017). Moreover, the BN approach has an easy-to-understand and easy-to-interpret interface which demonstrates the causal relationship among variables in a transparent manner (Correa et al., 2009). To date, the number of studies using this approach to assess the risk of WMSDs is limited. To the best of the authors’ knowledge, the study carried out by Thanathornwong et al. (2014) is the only research to have used this approach for predicting
Fig. 7. The trend of changes in the risk scores obtained from the REBA (the solid line) and FBnREBA (the dash line).
Fig. 8. The worst working posture of an operator from a desktop gas cooker assembly line.
This case study was conducted to determine the risk of WMSDs and to demonstrate which body segment should receive particular attention in order to reduce the WMSDs risk. The image in Fig. 8 was the worst posture in the assembly line. The angles of various body segments can be determined using Digimizer software or a goniometer. Since the position of various body segments were determined, the state of each segment was assigned using the fuzzy membership sets depicted in Figs. 3 and 4. Based on our measurements, the neck position was at 18◦ flexion, which belonged to two fuzzy membership sets: f1 with a membership degree of 0.8 and f2 with a membership degree of 0.2. Consequently, the “neck angle” node in FBnREBA was 80% at the “f1” state and 20% at the “f2” state, as demonstrated in Fig. 9. The same procedure was used to determine the states of other variables. 9
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
Fig. 9. Analysis of the working posture using FBnREBA.
the effectiveness of such interventions. REBA was developed to be a quick and easy-to-administer tool; FBnREBA is software-based, so the use of it is also easy and quick. As previously mentioned, many researchers and practitioners employ REBA and other similar methods to demonstrate the effectiveness of ergo nomic interventions in improving the working postures of individuals. In such situation, the advantages of being quick and easy are the second priority, and sensitivity to changes in input variables is the main inter est. The new method is more helpful in such situations, because it is more sensitive to input variables The new scoring system can solve some of the problems in previous studies in which demonstrating the effec tiveness of practical improvements in the workstation design using the risk scores of traditional methods was found to be difficult (Choobineh et al., 2004; Sanchez-Lite et al., 2013). The present study further revealed that by using predictive reasoning, the effectiveness of each intervention strategy can be deter mined; i.e. how the modification of each body segment can affect the risk of WMSDs can be determined. In contrast, using REBA, this process would be very time-consuming, because the REBA worksheet should be completed separately for each intervention. However, it should be mentioned that there are other factors to be considered in selecting and implementing an intervention. Cost, feasibility, and acceptability are some of them. Cost indicates the cost of implementing the intervention; feasibility shows how easy the intervention is to be implemented; and acceptability shows the acceptance of intervention by the workers (Chen et al., 2014). Moreover, the modification of a body part can have a negative impact on the posture of other body segments (Choobineh et al., 2004), and this issue should also be considered when selecting intervention options.
Table 3 Prior and posterior risk scores obtained from FBnREBA and REBA based on the modification of various body segments. Intervention Neck position Trunk position Upper arm Lower arm position Wrist position
Prior risk score
Posterior risk score
Risk reduction
FBnREBA
REBA
FBnREBA
REBA
FBnREBA
REBA
7.99 7.99 7.99 7.99
6 6 6 6
7.63 5.43 4.87 7.8
6 4 4 6
0.36 2.56 3.12 0.19
0 2 2 0
7.99
6
7.08
5
0.91
1
WMSDs risk. Their study was conducted on dentists, and various factors including gender, BMI, vibration, duration, and body posture were included in the BN model. The comparisons performed among the hypothetical postures demonstrated that, in contrast to REBA, a slight change in the score of any input variable affects the final FBnREBA risk score. Likewise, a huge change in body posture causes a considerable change in the final FBnREBA score. This is a very important advantage for several reasons. First, in contrast to traditional methods, it is very unlikely that exactly similar risk scores will be obtained for different postures. Secondly, it would be possible to show the effect of any modification in workstation design on the risk of WMSDs. It should be noted that REBA and other similar methods are both used in research and practice to demonstrate the effectiveness of ergonomic interventions in improving the working posture of individuals. For example, Pillastrini et al. (2010) employed REBA to demonstrate the effectiveness of ergonomic interventions in improving the working posture of video display terminal operators. In another study, Choobineh et al. (2004) used RULA to assess the effec tiveness of a newly designed workstation in correcting the working posture of carpet menders. Likewise, Ratzon et al. (2016) also utilized this approach to assess the effect of ergonomic interventions on the working postures of nurses. As FBnREBA has more sensitivity to changes in input variables, it can be more helpful and accurate in demonstrating
5. Conclusion The new scoring system developed based on fuzzy set theory and BN was more sensitive than traditional methods to changes in input vari ables. The new method is also capable of predicting the effectiveness of various interventions in reducing the risk of WMSDs. 10
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
Declaration of competing interest
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Authors declare that there is no conflict of interest regarding this paper. Acknowledgement The authors would like to thank Hamadan university of medical sciences for financial support (grant number: 9610196743). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.ergon.2020.103058. References Abolbashari, M.H., Chang, E., Hussain, O.K., Saberi, M., 2018. Smart Buyer: a Bayesian Network modelling approach for measuring and improving procurement performance in organisations. Knowl. Base Syst. 142, 127–148. Ahn, G., Hur, S., Jung, M.C., 2018. Bayesian network model to diagnose WMSDs with working characteristics. Int. J. Occup. Saf. Ergon. 1–38. https://doi.org/10.1080/ 10803548.2018.1502131, 0. Akbari, Hamed, Ghasemi, F., Akbari, Hesam, Adibzadeh, A., 2018. Predicting needlestick and sharp injuries and determining preventive strategies using Bayesian network approach, Iran. Epidemiol. Health 40, e2018042. Chen, J.-C., Lin, S.-C., Li, W.-C., 2014. Utilization of Human Factors Intervention Matrix (HFIX) to develop aviation safety management strategy. In: WMSCI 2014 - 18th World Multi-Conference Syst. Cybern. Informatics, Proc, vol. 2, pp. 50–55. Chiasson, M. `eve, Imbeau, D., Aubry, K., Delisle, A., 2012. Comparing the results of eight methods used to evaluate risk factors associated with musculoskeletal disorders. Int. J. Ind. Ergon. 42, 478–488. https://doi.org/10.1016/j.ergon.2012.07.003. Choobineh, A., Tosian, R., Alhamdi, Z., Davarzanie, M., 2004. Ergonomic intervention in carpet mending operation. Int. J. Ind. Ergon. 35, 493–496. https://doi.org/10.1016/ j.apergo.2004.01.008. Correa, M., Bielza, C., Pamies-Teixeira, J., 2009. Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process. Expert Syst. Appl. 36, 7270–7279. https://doi.org/10.1016/j.eswa.2008.09.024. David, G.C., 2005. Ergonomic methods for assessing exposure to risk factors for workrelated musculoskeletal disorders. Occup. Med. 55, 190–199. https://doi.org/ 10.1093/occmed/kqi082. Dockrell, S., O’Grady, E., Bennett, K., Mullarkey, C., Mc Connell, R., Ruddy, R., Twomey, S., Flannery, C., 2012. An investigation of the reliability of Rapid Upper Limb Assessment (RULA) as a method of assessment of children’s computing posture. Appl. Ergon. 43, 632–636. Enez, K., Nalbanto˘ glu, S.S., 2019. Comparison of ergonomic risk assessment outputs from OWAS and REBA in forestry timber harvesting. Int. J. Ind. Ergon. 70, 51–57. https:// doi.org/10.1016/j.ergon.2019.01.009. Ghasemi, F., Kalatpour, O., Moghimbeigi, A., Mohammadfam, I., 2017. Selecting strategies to reduce high-risk unsafe work behaviors using the safety behavior sampling technique and bayesian network analysis. J. Res. Health Sci. 17, e00372. Golabchi, A., Han, S., Fayek, A.R., 2016. A fuzzy logic approach to posture-based ergonomic analysis for field observation and assessment of construction manual operations. Can. J. Civ. Eng. 43, 294–303. https://doi.org/10.1139/cjce-2015-0143. Gupta, P., Kulkarni, N., 2013. An introduction of soft computing approach over hard computing. Int. J. Latest Trends Eng. Technol. 3, 254–258. Hashim, A., Dawal, S.Z., Yusoff, N., 2012. Ergonomic evaluation of postural stress in school workshop. Work 41, 827–831. https://doi.org/10.3233/WOR-2012-0249827. Hignett, S., McAtamney, L., 2000. Rapid entire body assessment (REBA). Appl. Ergon. 31, 201–205. Jahanimoghadam, F., Horri, A., Hasheminejad, N., Hashemi Nejad, N., Baneshi, M.R., 2018. Ergonomic evaluation of dental professionals as determined by rapid entire body assessment method in 2014. J. Dent. 19, 155–158. Jamshidi, A., Yazdani-chamzini, A., Haji, S., 2013. Developing a new fuzzy inference system for pipeline risk assessment. J. Loss Prev. Process. Ind. 26, 197–208.
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International Journal of Industrial Ergonomics journal homepage: http://www.elsevier.com/locate/ergon
A new scoring system for the Rapid Entire Body Assessment (REBA) based on fuzzy sets and Bayesian networks Fakhradin Ghasemi a, *, Neda Mahdavi b a
Department of Ergonomics, Center of Excellence for Occupational Health, Occupational Health and Safety Research Center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran b Department of Ergonomics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
A R T I C L E I N F O
A B S T R A C T
Keywords: Risk assessment Bayesian network Work-related musculoskeletal disorders
Traditional methods for assessing the risk of work-related musculoskeletal disorders (WMSDs) have a low sensitivity to changes in input variables. Using them, it is possible to obtain the same risk score for totally different postures, and in some cases, the effectiveness of ergonomic interventions cannot be demonstrated. This study aimed to develop a new scoring system for REBA, FBnREBA, using fuzzy sets and the Bayesian network (BN) approach to cover the drawbacks of the traditional REBA. First, the risk factors of WMSDs were defined in terms of fuzzy membership sets. Next, a BN model was developed based on REBA. Fourteen different postures were assessed using FBnREBA, and the results were compared with those of the original REBA. Lastly, a case study was performed to demonstrate how the new scoring system can be used to rank various interventions based on their effectiveness. FBnREBA is a BN model with 26 nodes and is based completely on REBA, but its results differ from those of REBA for identical postures. A comparison of the results of FBnREBA with those of REBA indicated that FBnREBA is more sensitive to changes in WMSDs risk factors than REBA. A case study was con ducted using FBnREBA, and the effectiveness of modifying each body segment was determined and ranked. FBnREBA is more sensitive to changes in input variables so that it is unlikely to obtain the same risk score for different body postures. The introduced methodology can be used to modify the scoring systems of other similar methods.
1. Introduction 1.1. Methods for assessing the risk of work-related musculoskeletal disorders Work-related musculoskeletal disorders (WMSDs) are a major problem for both employers and employees in occupational settings and should be managed properly (Morse et al., 1998; Stewart et al., 2003). Risk assessment is the main tool for identifying jobs or tasks which are more prone to WMSDs. Using risk assessment, the level of exposure to various WMSDs risk factors is determined and improvement in terventions are prioritized. Methods and techniques for assessing the risk of WMSDs have been categorized into four main groups: pen and paper observational methods, videotaping and computer-assisted analyses, direct or instru mental techniques, and self-report assessment tools (Li and Buckle,
1999). Pen and paper observational methods are most popular, because they are easy to learn, easy to implement, inexpensive, and less time-consuming. OWAS (Ovako Working Analysis System) (Karhu et al., 1977), RULA (Rapid Upper Limb Assessment) (McAtamney and Corlett, 1993), and REBA (Rapid Entire Body Assessment) (Hignett and McA tamney, 2000) are among the most popular pen and paper observational methods. 1.2. Drawbacks of traditional methods Although these methods are very useful in providing deep insight into the risk of WMSDs, they have several drawbacks. One main draw back of traditional methods such as REBA is their low sensitivity to input variables. For example, with traditional methods, different postures may result in the same risk score, even though they impose different biomechanical effects on the body. For example, according to REBA,
* Corresponding author. Hamadan University of Medical Sciences, School of Public Health, Ergonomics Department, 65178-3-8736, Shahid Fahmideh BLVD, Hamadan, Hamadan province, Iran. E-mail address: [email protected] (F. Ghasemi). https://doi.org/10.1016/j.ergon.2020.103058 Received 13 November 2018; Received in revised form 22 February 2020; Accepted 30 October 2020 Available online 10 November 2020 0169-8141/© 2020 Elsevier B.V. All rights reserved.
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
there is no difference between trunk scores at 21◦ flexion and 59◦ flexion, even though the biomechanical stresses imposed on the body at these two postures are totally different. In contrast, when the upper arm position is at 44◦ flexion, the risk score is 2, but when it is at 46◦ flexion, the risk score is 3, which may result in different final REBA scores, even though the difference between these two postures does not seem to be significant. The scores associated with load/force suffer from the same problem. For example, there is no difference between a 12-lb load and a 22-lb load (both of them receive a score of 1), while the biomechanical stress they impose on the body can be totally different. This drawback has been mentioned in several previous studies (David, 2005; Golabchi et al., 2016; Madani and Dababneh, 2016) and may diminish the inter-observer reliability of traditional methods, particularly when the position of a body segment is close to the boundary between two ranges. The low inter-observer reliability of REBA, RULA, and other traditional methods have been mentioned by several studies, including Schwartz et al. (2019), Dockrell et al. (2012), and David (2005). In confirmation of these issues, McAtamney and Corlett (1993) explained that when the position of a body segment is close to the boundaries of ranges, some discrepancies may be observed in the result of risk assessment. Low sensitivity to input variables makes it difficult to demonstrate the effectiveness of ergonomic interventions. For example, assume an intervention modifies the trunk position by altering its flexion angle from 59◦ to 20◦ . This improvement has no effect on the final RULA or REBA score. This problem has also been mentioned in previous studies, such as Choobineh et al. (2004) and Sanchez-Lite et al. (2013). Simi larly, Li and Buckle (1999) stated that an acceptable ergonomic assess ment tool should be able to demonstrate the effectiveness of an ergonomic intervention. Accordingly, several studies have been conducted to develop new methods for covering these drawbacks. For example, Sanchez-Lite et al. (2013) developed the Novel Ergonomic Postural Assessment Method (NERPA) as an alternative of RULA. Golabchi et al. (2016) recom mended the use of fuzzy set theory to deal with such deficiencies and developed a fuzzy logic model based on RULA for assessing the ergo nomic risks in manual construction operations. They showed their method to be more accurate and able to reduce the probability of human error during assessment. Rivero et al. (2016) built a Fuzzy Inference System (FIS) based on RULA for WMSDs risk assessment. Pavlovic-Ve selinovic et al. (2016) developed the fuzzy expert system known as SONEX to predict WMSDs and suggest preventive interventions. In another study, Nunes (2009) developed the fuzzy expert system FAST ERGO X for auditing, assessing, and controlling exposure to ergonomic risk factors in workplaces. In the same vein, Ahn et al. (2018) con structed a diagnostic model of WMSDs useful in assessing the effects of various working characteristics such as working hours and work pace on WMSDs. They also reported that BN was stronger than artificial neural network, support vector machine, and decision tree approaches in assessing risk factors and predicting WMSDs.
body parts as well as all well-known WMSDs risk factors, i.e. awkward posture, force, and types of activity (repetitive and static). This method provides a better definition for neutral postures than RULA (Madani and Dababneh, 2016) and is appropriate for both static and dynamic tasks (Hashim et al., 2012). The intra- and inter-observer reliability of REBA has been demonstrated to be satisfactory (Hignett and McAtamney, 2000; Kee and Karwowski, 2007). However, similar to most ergonomic assessment tools, the predictive and concurrent validity of this method needs to be further elucidated in future studies (Madani and Dababneh, 2016). Since its invention, the method has been utilized extensively for ergonomic assessment in various sectors, including oil industries (Motamedzade et al., 2011), hospital nursing (Ratzon et al., 2016), dentistry (Jahanimoghadam et al., 2018), mining industries (Norhi ˘lu, dayah et al., 2016), forestry timber harvesting (Enez and Nalbantog 2019), sewing machine operation (Sakthi Nagaraj et al., 2019), and other sectors (Chiasson et al., 2012). A recent study showed that the use of this method is increasing among ergonomists (Lowe et al., 2019). It should be emphasized that the new methodology can also be used to modify the scoring system of other methods such as RULA. REBA equipped with the new scoring system will be called FBnREBA for the rest of the study. 2. Materials and methods 2.1. REBA method This method, first introduced by Hignett and McAtamney (2000), divides the body into two main parts; the first part is composed of the neck, trunk, and legs. Their scores are combined using Table A in the REBA worksheet to obtain a single value. The second part is composed of upper arm, lower arm, and wrist, and their scores are aggregated using Table B in the REBA worksheet. After adding the scores associated with coupling and force, the scores of these tables are combined using Table C. Lastly, the score associated with the type of activity is added. The final REBA score has a range from one to greater than eleven; the higher the final score is, the greater the risk of WMSDs will be. These scores and associated action levels are presented in Table 1. 2.2. Fuzzy set theory Fuzzy set theory, first introduced by Zadeh (1965), is a soft computing technique that is very helpful in dealing with the vagueness, partial truth, and uncertainty present in solving a problem (Gupta and Kulkarni, 2013). According to this theory, a single value can belong to several sets with different degrees of membership. Degree of member ship can adopt any value within the range of zero to one, which is against the classical view of sets in which the degree of membership of a variable to a set can only be either 0 or 1 (Jamshidi et al., 2013; Klir and Yuan, 1996). In the present study, fuzzy set theory was used to deal with the sudden changes in angle ranges of a body segment. Using this theory, the transition from one range to the next was gradual, which modified the traditional way of scoring input variables. For example, assume that the angle ranges of an upper arm position are defined using fuzzy mem bership functions as demonstrated in Fig. 1, and the angle of the upper arm in a specific body posture is at 18◦ flexion. When using traditional
1.3. The present study The low sensitivity of traditional methods to changes in input vari ables stems from the fact that they commonly follow the principles of classical (crisp) set theory. A method developed based on these princi ples would lead to sharp boundaries between the angle ranges of a body segment. Consequently, the final risk score suffers from low sensitivity to changes in input variables, thereby leading to inaccurate results in some cases. This drawback can be properly handled by fuzzy set theory (Zadeh, 1965) and Bayesian networks (BNs) (Pearl, 2014). Therefore, the main aim of the present study was to develop a new scoring methodology based on fuzzy set theory and BNs to modify pre vious methods so as to increase their sensitivity to input variables. REBA was selected to demonstrate how the methodology should be implemented. This method was selected because it is general in nature and applicable to a wide range of occupations. Moreover, it considers all
Table 1 The final risk score in the REBA method and associated action levels.
2
REBA score
Action level
1 2–3 4–7 8–10 >11
No action required Modification may be needed Further investigation may be needed Modification is required Modification is required immediately
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
Fig. 1. The process of translating the angle ranges of traditional REBA into fuzzy membership functions.
Fig. 2. The process of constructing FBnREBA based on REBA. 3
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
REBA, which is based on the crisp set theory, the score associated with this body segment would be 1. In contrast, when the fuzzy set theory is used, this angle of flexion belongs to two sets and consequently adopts two scores with different degrees of membership; its scores are 1 and 2 with 0.1 and 0.9 degrees of membership, respectively. In the present study, all input variables of REBA were translated into fuzzy membership functions using the process known as “fuzzification.” Triangular fuzzy membership sets were used in this step. There are other types of fuzzy membership functions to be used; however, the calcula tions associated with triangular membership sets for determining membership degrees are simpler than those of other fuzzy membership functions.
It should be mentioned that GENIE software developed by Bayes Fusion (www.bayesfusion.com) was used to perform all modeling and analyses related to BN. 2.4. Calculating the final risk score of WMSDs based on FBnREBA When the original REBA method is used, the final risk score is a single integer value in the range of 1–13. When FBnREBA is used, however, the result is a set of integer values with different probabilities. To transform them into a single value, their weighted sum should be calculated using the following equation: ∑ RS = Pi Si
2.3. Bayesian networks (BNs)
where RS stands for the final risk score, Pi is the probability associated with state i, and Si is the score associated with state i.
When the input variables of REBA or any similar method are expressed in terms of fuzzy membership functions, tables presented in the REBA or RULA worksheets (such as Tables A, B, and C in the REBA worksheet) can no longer be used for merging the scores of various body segments. Instead, studies have generally used fuzzy inference system (FIS), which contains a set of rules that create relationships between input variables and the output variable (Jamshidi et al., 2013). This property of fuzzy set theory was not used in the present study, because it needs a high number of rules which are tedious for both determining and inserting into the inference engine. The BN approach was used in this study to determine the relationships between the input variables and the risk of WMSDs. BNs are graphical analytical tools which have been used extensively in modeling and assessing risk in complex systems (Jensen and Nielsen, 2009). A BN is composed of a set of nodes representing the variables and a set of directed arcs depicting the causal relationships among the nodes (Ghasemi et al., 2017; Jensen and Nielsen, 2009). Each BN has a quantitative part known as a conditional probability table (CPT) that reflects how a variable is affected by its parents. In the other words, CPTs determine how a variable would change if its parents change (Jensen and Nielsen, 2009). To benefit from this modeling approach, the qualitative part, i.e. nodes and directed arcs, and quantitative part, i.e. CPTs, of the model must be determined. In this study, both of these parts were determined using the information extracted from the original REBA. To determine the structure of the BN, all input variables and the A, B, and C tables in the REBA worksheet were regarded as the nodes constituting the BN. As the score of each body segment was determined using its fuzzified angle ranges and the required adjustments, several new nodes related to the posture of various body segments and the required adjustments were also added. This process for determining a trunk score is depicted in Fig. 1, part A. For merging the risk scores of the neck, trunk, and legs, Table A from the REBA worksheet was regarded as a node in the BN model (see Fig. 2, part B). Likewise, for merging the risk scores of upper arm, lower arm, and wrist, Table B from the REBA worksheet was regarded as a new node and inserted in the BN model. To determine the CPTs of nodes representing the tables from the REBA worksheet, the same principles as the original REBA were used. For example, Table A in the REBA worksheet contains values ranging from 1 to 9, so the “score A′′ node in the BN model has nine states demonstrated by s1, s2, s3 … and s9. For assigning the CPT of this node, the same approach as Table A was adopted. For example, assume that a configuration of states of the neck, trunk, and leg positions corresponds to score 5 in Table A (based on the REBA worksheet). To determine the CPT of the “score A′′ node for this configuration, all states of “score A′′ were given a probability of zero, while state s5 received a probability one. An example of this process is presented in Fig. 2 (part C). In this figure, the CPT of Table B was built in the BN. Because the CPT of this node was too big in size to be presented, just that part which corresponds to that part of Table B, discriminated by a red rectangle, is presented (Fig. 2, part C).
2.5. Comparison of REBA and FBnREBA In this step, several postures were analyzed using both REBA and FBnREBA, and the differences were investigated. 2.6. Determining the most effective intervention strategy using BN reasoning One of the main characteristics of a BN is its ability to perform various types of reasoning, such as predictive, diagnostic, inter-causal, and combined reasoning (Abolbashari et al., 2018). The most effective way to reduce the risk of WMSDs can be determined using predictive reasoning. Accordingly, a real case was investigated, and the risk of WMSDs was determined using FBnREBA. Next, predictive reasoning was employed to determine the effectiveness of various interventions in reducing the final FBnREBA score. The case study was conducted in a desktop gas cooker assembly line. The line suffers from poor ergonomic conditions, and we wanted to know which intervention strategy was more beneficial than the others. Several postures were selected, and the worst one was analyzed using FBnREBA. Then, the intervention options were compared based on their effectiveness in reducing the risk of WMSDs. 3. Results 3.1. Fuzzification of input variables Figs. 3 and 4 demonstrate the fuzzy membership functions associated with various input variables of REBA. It should be stressed that the fuzzification process was not possible for some variables such as the position of the legs, coupling score, and activity score; thus, they were treated the same as the original REBA. Moreover, each fuzzy membership set can be expressed using a set of equations. Equations (1)–(5) are associated with upper arm angles. ⎧ ⎫ 1 x > 90 ⎨ ⎬ f (x)f 4 = (x − 50)/40 45 ≤ x ≤ 90 (1) ⎩ ⎭ 0 x < 45 ⎧ ⎪ ⎪ ⎨
⎫ 0 x > 90 ⎪ ⎪ ⎬ (90 − x)/45 45 < x ≤ 90 f (x)f 3 = 20 < x ≤ 45 (x − 20)/45 ⎪ ⎪ ⎪ ⎪ ⎩ ⎭ 0 x < 20 ⎧ ⎪ ⎪ ⎨
⎫ 0 x > 20 ⎪ ⎪ ⎬ (45 − x)/25 20 < x ≤ 45 f (x)f 2 = (x + 20)/20 0 < x ≤ 20 ⎪ ⎪ ⎪ ⎪ ⎩ ⎭ 0 x<0
4
(2)
(3)
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
Fig. 3. The fuzzy membership sets associated with force and positions of neck and trunk.
⎧ ⎪ ⎪ ⎨
⎫ 0 x > 20 ⎪ ⎪ ⎬ (20 − x)/20 0 < x ≤ 20 f (x)f 1 = (x + 20)/20 0 < x ≤ − 20 ⎪ ⎪ ⎪ ⎪ ⎩ ⎭ 0 x < − 20 ⎧ ⎨
⎫ 0 x>0 ⎬ f (x)e = (− x)/20 0 ≤ x ≤ − 20 ⎩ ⎭ 1 x < − 20
3. Trunk angle: This node represents the angle of trunk flexion. It has five states: e, f1, f2, f3, and f4. These states are associated with the fuzzy membership sets depicted in Fig. 3. 4. Trunk adjustment: This node represents any adjustment required for assigning the trunk score, including trunk twisting and side bending. This node has two states: no and yes. State no indicates that there is no need for adjustment; state yes indicates that adjustment is required. 5. Legs position score: This node represents the score of the legs, which can range from 1 to 4. The node has four states: s1, s2, s3, and s4 corresponding to scores 1, 2, 3, and 4, respectively. 6. Force: This node represents the amount of force required for performing a task. It has three states: s1, s2, and s3. The fuzzy membership functions associated with these states are presented in Fig. 3. 7. Force adjustment: This node is used to make any adjustment required for assigning the score of force, such as shock or rapid buildup of force. This node has two states, yes and no, explaining the need for adjustment. 8. Coupling score: This node demonstrates the score of coupling. The node has four states, s0, s1, s2, and s3, corresponding to scores of 0, 1, 2, and 3, respectively.
(4)
(5)
3.2. BN model constructed based on REBA method The BN constructed based on the REBA method had a total number of 26 nodes. A brief description of each node is presented below: 1. Neck angle: This node represents the angle of the neck flexion. It has three states: e, f1, and f2. These states are associated with the fuzzy membership sets depicted in Fig. 3. 2. Neck adjustment: This node represents any adjustment required for assigning the neck score, which includes neck twisting and side bending. It has two states: no and yes. State no indicates that there is no need for adjustment; state yes indicates that adjust ment is required. 5
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
Fig. 4. The fuzzy membership sets associated with positions of upper arm, lower arm, and wrist.
9. Upper arm angle: This node demonstrates upper arm angle flexion. The node has five states: e, f1, f2, f3, and f4. The fuzzy membership functions associated with these states are presented in Fig. 4. 10 . Upper arm adjustment: This node is used to insert the effect of any adjustment associated with the upper arm position on the upper arm score. The node has four states, s_1, s0, s1, and s2, corresponding to scores − 1, 0, 1, and 2. 11. Lower arm angle: This node demonstrates the lower arm angle and has three states: h_e, n, h_f. The fuzzy membership functions associated with this node are presented in Fig. 4. 12. Wrist angle: This node demonstrates the wrist angle and has three states: h_e, n, and h_f. The fuzzy membership functions associated with these states are presented in Fig. 4. 13. Wrist adjustment: This node demonstrates any adjustment required for assigning the score of the wrist. These adjustments are required when the wrist is twisted or is deviated from the midline. The node has two states, yes and no, demonstrating the need for any adjustment. 14. Trunk score: This node demonstrates the final score associated with trunk posture, which should be determined by considering the states of its parent nodes, i.e. “trunk angle” and “trunk adjustment.” The node has five states, s1, s2, s3, s4, and s5,
15.
16.
17.
18. 19.
6
corresponding to scores 1, 2, 3, 4, and 5, respectively. The CPT of this node is presented in Fig. 5. Neck score: This node represents the final score of the neck and is determined by its angle and required adjustment. The node has three states, s1, s2, and s3, corresponding to the scores of 1, 2, and 3. The CPT of this node is presented in Fig. 5. Force/load score: This node demonstrates the final score of force which is affected by its amount and required adjustment. The node has four states, s0, s1, s2, and s3, corresponding to scores 0, 1, 2, and 3. The CPT of this node is presented in Fig. 5. Upper arm score: This node demonstrates the final score of upper arm and is determined by its parent nodes, i.e. “upper arm angle” and “upper arm adjustment.” It also has six states, s1, s2, s3, s4, s5, and s6, corresponding to scores 1, 2, 3, 4, 5, and 6, respectively. Lower arm score: This node demonstrates the final score of the lower arm and is determined by its angle. The node has only two states, s1 and s2, corresponding to scores 1 and 2, respectively. Wrist score: This node demonstrates the final score of the wrist and is determined by its angle and required adjustments. The node has three states, s1, s2, and s3, corresponding to scores 1, 2, and 3, respectively. The CPT of this node is presented in Fig. 5.
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
Fig. 5. CPTs of several nodes of the BN model constructed based on the REBA.
Fig. 6. The BN model constructed based on the REBA method. 7
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
20. Score A: This node represents Table A from the REBA worksheet and is used to integrate the scores of the neck, trunk, and legs. It has nine states, s1, s2, s3 … s9, corresponding to scores 1, 2, 3 … 9, respectively. 21. Adjusted score A: This node is created to integrate the score ob tained from Table A with that of force/load. It has 12 states, s1, s2, s3 … s12, corresponding to scores 1, 2, 3 … 12, respectively. 22. Score B: This node represents Table B from the REBA worksheet and is used to integrate the scores of the upper arm, lower arm, and wrist. The node has nine states, s1, s2, s3 … s9, corre sponding to scores 1, 2, 3 … 9, respectively. 23. Adjusted score B: This node is created to integrate the scores of Table B and the coupling type. Similar to the “adjusted score A′′ node, this node has 12 states, s1, s2, s3 … s12, corresponding to scores 1, 2, 3 … 12, respectively. 24. Table C score: This node represents Table C from the REBA worksheet and is used to integrate the scores of the two nodes of “adjusted score A′′ and “adjusted score B". This node has 12 states, s1, s2, s3 … s12, corresponding to scores 1, 2, 3 … 12, respectively. 25. Activity score: This node demonstrates activity type and has two states, s0 and s1, corresponding to scores 0 and 1, respectively. When the activity is repetitive or static, the state of the node is s1; otherwise, it is s0. 26. WMSDs risk: This node demonstrates the final risk of WMSDs. It has 13 states, s1, s2, s3 … s13, corresponding to scores 1, 2, 3 … 13.
• Comparison of Posture II and Posture III: These two postures are also similar in all body regions except the position of the neck. The angles of the neck flexion in Posture II and Posture III are 10◦ and 19◦ , respectively. In contrast to REBA which provides the same risk score for these two postures, FBnREBA discriminates between them by providing two different risk scores: 4.81 for Posture II, and 6.74 for Posture III. • Comparison of Posture III and Posture IV: The only difference be tween these two postures is the angle of the trunk flexion, which is 2◦ higher in Posture IV than in Posture III. It is clear that the biome chanical stress of Posture III is slightly greater than that of Posture IV. The REBA method is unable to demonstrate this gentle difference, whereas FBnREBA differentiates between them by providing slightly different risk scores. • Comparison of Posture IV and Posture V: The only difference be tween these two postures is the angle of the trunk flexion, which is 21◦ for Posture IV and 59◦ for Posture V. Similar to previous pair comparisons, REBA cannot discriminate between these two postures, whereas FBnREBA provides different risk scores for them. • Comparison of Posture V and Posture VI: The only difference be tween these two postures is the angle of the neck flexion, which is slightly higher in Posture VI. REBA demonstrates this difference by providing a huge difference in risk scores for the two postures. An increase of 2◦ in the angle of the neck flexion increases the REBA score from 5 to 7, whereas FBnREBA treats this slight change in a more rational manner such that the risk scores for these postures are very close together. • Comparison of Posture VII and Posture VIII: The difference between these two postures is an increase of 2◦ in the angle of the trunk flexion. REBA reacts to this change by increasing the risk score from 7 to 8; however, there is a slight difference between risk scores provided by FBnREBA, which is more rational. • Comparison of Posture XIII and Posture XIV: What stands out in this comparison is the effect of force/load on the risk scores provided by REBA and FBnREBA. When the amount of force increases from 1 lb to 10 lbs, there is no change in the risk scores provided by REBA, while the risk score provided by FBnREBA increases from 10.1 to 10.9.
By implementing the above-mentioned methods, the BN model demonstrated in Fig. 6 was obtained. 3.3. Comparison of REBA and FBnREBA For comparison purposes, 14 different hypothetical postures (Table 2) were made and assessed using both REBA and FBnREBA. The two leftmost columns of this table demonstrate the results of these as sessments. To demonstrate the differences between these two methods, the following comparisons were made:
From the above comparisons, it can be inferred that FBnREBA is more sensitive to changes in input variables. The trend of changes in the risk scores obtained from these two methods is demonstrated in Fig. 7. As can be seen, the trends are ascending in both methods. However, the final REBA score increases in a stepwise manner, whereas the final FBnREBA score increases with a steady slope.
• Comparison of Posture I and Posture II: These two postures are similar to each other in all body segments except the trunk position. In Posture I, the trunk angle is at 1◦ flexion, while in Posture II, the trunk angle is at 19◦ flexion. These two postures exert different biomechanical effects on the body. 3D modeling using CATIA soft ware package showed that the L4/L5 compression forces for these two angles are 580N and 1384N, respectively. Although REBA is unable to discriminate between the risk scores of these two postures, FBnREBA provides two different risk scores for them.
3.4. Case study A case study was conducted in a desktop gas cooker assembly line.
Table 2 Characteristics of fourteen postures used for comparing FBnREBA and REBA. Posture
Neck angle
Trunk angle
Legs score
Force
Upper arm angle
Lower arm angle
Wrist position
adjustments
REBA score
FBnREBA
I II III IV V VI VII VIII IX X XI XII XIII XIV
10◦ 10◦ 19◦ 19◦ 19◦ 21◦ 21◦ 21◦ 21◦ 21◦ 21◦ 21◦ 21◦ 21◦
1◦ 19◦ 19◦ 21◦ 59◦ 59◦ 59◦ 61◦ 61◦ 61◦ 61◦ 61◦ 61◦ 61◦
2 2 2 2 2 2 2 2 2 2 2 2 2 2
1 lb 1 lb 1 lb 1 lb 1 lb 1 lb 1 lb 1 lb 1 lb 1 lb 1 lb 1 lb 1 lb 10 lb
5◦ 5◦ 5◦ 5◦ 5◦ 5◦ 40◦ 40◦ 40◦ 40◦ 40◦ 40◦ 46◦ 46◦
80◦ 80◦ 80◦ 80◦ 80◦ 80◦ 80◦ 80◦ 99◦ 101◦ 101◦ 101◦ 101◦ 101◦
0◦ 0◦ 0◦ 0◦ 0◦ 0◦ 0◦ 0◦ 0◦ 0◦ 14◦ 16◦ 16◦ 16◦
Trunk Trunk Trunk Trunk Trunk Trunk Trunk Trunk Trunk Trunk Trunk Trunk Trunk Trunk
5 5 5 5 5 7 7 8 8 8 8 9 10 10
4.19 4.81 6.74 6.93 7.98 7.99 8.87 8.90 9.28 9.32 9.78 9.80 10.1 10.9
8
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International Journal of Industrial Ergonomics 80 (2020) 103058
The FBnREBA score for the posture was 7.63, while the REBA score was 6. In the next step, the effects of modification of various body seg ments on the final risk score of REBA and FBnREBA was investigated, and the results are presented in Table 3. All modifications could reduce the final FBnREBA risk score to some extent. In contrast, REBA was unable to demonstrate the effects of modifications on the neck and the lower arm postures (the prior and posterior risk scores were equal). Moreover, in contrast to REBA, FBnREBA was able to differentiate the positive effects of modifications on the upper arm and trunk postures. Furthermore, as FBnREBA is totally software-based, it is easier and less time-consuming to use in assessing the effects of modifications on the final risk score. 4. Discussion The present study aimed to develop a new scoring system for REBA such that the final risk score would become more sensitive to the posi tion of body segments. Fuzzy sets and Bayesian network approaches were used in developing the new system. Previous studies have only used fuzzy set theory to solve such a deficiency in traditional methods. The reason behind the use of fuzzy set theory is obvious: to avoid the sharp boundaries between angle ranges of a body segment, which lead to sudden changes in the scoring of input variables. Traditional methods are based on principles of classical (crisp) set theory, and fuzzy set theory is able to cover the deficiencies of these principles. Today, the application of fuzzy set theory instead of classical methods is commonplace in other areas of research and practice. For example, Jamshidi et al. (2013) used it to modify the Kent-Muhlbauer method of pipeline risk assessment, and Kutlu and Ekmekçioglu (2012) employed the theory to apply soft computing principles in modifying failure modes and the effects analysis (FMEA) method. The most prominent difference between the present study and pre vious ones is that most previous studies utilized FIS to relate the input variables to the output variable. To develop FIS, several steps should be carried out, including fuzzification of inputs, development of the rule base (a set of If-Then rules), determination of the aggregation method, and selection of the defuzzification method (Jamshidi et al., 2013). There are several ways to perform each of these steps, and each way may lead to a different output. Interestingly, there is no universally-accepted criterion for selecting the most appropriate way to perform each step (Yazdi and Zarei, 2018). Furthermore, the rule base, as the heart of FIS, is commonly developed based on the opinions of domain experts. This approach has some potential drawbacks which can affect the final re sults. For example, how many experts should participate in developing the rule base? What is the best approach to integrating the knowledge of experts? Who can be considered an expert in the area of interest? Therefore, because of the above-mentioned issues, the modification of traditional methods using FIS may cause deviation from the principles used in the original method. When BN is used instead of FIS, none of the above-mentioned obstacles will be faced, because the CPTs of the BN are developed based on the tables presented in the original method. In other words, the tables used in the REBA worksheet for merging the scores of various parts of the body are exactly mapped in the CPTs of the BN model constructed based on REBA. The methodology presented in this study is unique and, to the best of the authors’ knowledge, this paper is the first to have utilized BN to improve classical methods. Using BN to assess the risk of exposure to ergonomic risk factors has several advantages. BN is very flexible and widely used to model complex systems based on their causal relation ships (Akbari et al., 2018; Ghasemi et al., 2017). Moreover, the BN approach has an easy-to-understand and easy-to-interpret interface which demonstrates the causal relationship among variables in a transparent manner (Correa et al., 2009). To date, the number of studies using this approach to assess the risk of WMSDs is limited. To the best of the authors’ knowledge, the study carried out by Thanathornwong et al. (2014) is the only research to have used this approach for predicting
Fig. 7. The trend of changes in the risk scores obtained from the REBA (the solid line) and FBnREBA (the dash line).
Fig. 8. The worst working posture of an operator from a desktop gas cooker assembly line.
This case study was conducted to determine the risk of WMSDs and to demonstrate which body segment should receive particular attention in order to reduce the WMSDs risk. The image in Fig. 8 was the worst posture in the assembly line. The angles of various body segments can be determined using Digimizer software or a goniometer. Since the position of various body segments were determined, the state of each segment was assigned using the fuzzy membership sets depicted in Figs. 3 and 4. Based on our measurements, the neck position was at 18◦ flexion, which belonged to two fuzzy membership sets: f1 with a membership degree of 0.8 and f2 with a membership degree of 0.2. Consequently, the “neck angle” node in FBnREBA was 80% at the “f1” state and 20% at the “f2” state, as demonstrated in Fig. 9. The same procedure was used to determine the states of other variables. 9
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
Fig. 9. Analysis of the working posture using FBnREBA.
the effectiveness of such interventions. REBA was developed to be a quick and easy-to-administer tool; FBnREBA is software-based, so the use of it is also easy and quick. As previously mentioned, many researchers and practitioners employ REBA and other similar methods to demonstrate the effectiveness of ergo nomic interventions in improving the working postures of individuals. In such situation, the advantages of being quick and easy are the second priority, and sensitivity to changes in input variables is the main inter est. The new method is more helpful in such situations, because it is more sensitive to input variables The new scoring system can solve some of the problems in previous studies in which demonstrating the effec tiveness of practical improvements in the workstation design using the risk scores of traditional methods was found to be difficult (Choobineh et al., 2004; Sanchez-Lite et al., 2013). The present study further revealed that by using predictive reasoning, the effectiveness of each intervention strategy can be deter mined; i.e. how the modification of each body segment can affect the risk of WMSDs can be determined. In contrast, using REBA, this process would be very time-consuming, because the REBA worksheet should be completed separately for each intervention. However, it should be mentioned that there are other factors to be considered in selecting and implementing an intervention. Cost, feasibility, and acceptability are some of them. Cost indicates the cost of implementing the intervention; feasibility shows how easy the intervention is to be implemented; and acceptability shows the acceptance of intervention by the workers (Chen et al., 2014). Moreover, the modification of a body part can have a negative impact on the posture of other body segments (Choobineh et al., 2004), and this issue should also be considered when selecting intervention options.
Table 3 Prior and posterior risk scores obtained from FBnREBA and REBA based on the modification of various body segments. Intervention Neck position Trunk position Upper arm Lower arm position Wrist position
Prior risk score
Posterior risk score
Risk reduction
FBnREBA
REBA
FBnREBA
REBA
FBnREBA
REBA
7.99 7.99 7.99 7.99
6 6 6 6
7.63 5.43 4.87 7.8
6 4 4 6
0.36 2.56 3.12 0.19
0 2 2 0
7.99
6
7.08
5
0.91
1
WMSDs risk. Their study was conducted on dentists, and various factors including gender, BMI, vibration, duration, and body posture were included in the BN model. The comparisons performed among the hypothetical postures demonstrated that, in contrast to REBA, a slight change in the score of any input variable affects the final FBnREBA risk score. Likewise, a huge change in body posture causes a considerable change in the final FBnREBA score. This is a very important advantage for several reasons. First, in contrast to traditional methods, it is very unlikely that exactly similar risk scores will be obtained for different postures. Secondly, it would be possible to show the effect of any modification in workstation design on the risk of WMSDs. It should be noted that REBA and other similar methods are both used in research and practice to demonstrate the effectiveness of ergonomic interventions in improving the working posture of individuals. For example, Pillastrini et al. (2010) employed REBA to demonstrate the effectiveness of ergonomic interventions in improving the working posture of video display terminal operators. In another study, Choobineh et al. (2004) used RULA to assess the effec tiveness of a newly designed workstation in correcting the working posture of carpet menders. Likewise, Ratzon et al. (2016) also utilized this approach to assess the effect of ergonomic interventions on the working postures of nurses. As FBnREBA has more sensitivity to changes in input variables, it can be more helpful and accurate in demonstrating
5. Conclusion The new scoring system developed based on fuzzy set theory and BN was more sensitive than traditional methods to changes in input vari ables. The new method is also capable of predicting the effectiveness of various interventions in reducing the risk of WMSDs. 10
F. Ghasemi and N. Mahdavi
International Journal of Industrial Ergonomics 80 (2020) 103058
Declaration of competing interest
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