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Ergonomics

The aim of this study is to investigate the isometric strength or MVC (maximum voluntary contraction) by measuring grip strength, arm strength, leg strength, torso strength and key pinch strength.So that each participant’s strength percentile values in the population for each strength category can be calculated and human biomechanical capabilities and relative variability in human capabilities can be understood by comparing the data collected.Also the effects of the factors, like grip span, wrist posture or using the dominant side, on each of the strength categories can be examined.

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. INTRODUCTION Isometric strength is the force that a muscle group can exert without movement. Maximum Isometric strength is the maximum force that a muscle group can exert without movement. It is also called MVC (maximum voluntary contraction). Some of the widely used isometric strengths are: arm strength, shoulder strength, composite (leg) strength, back (torso) strength, grip strength and pinch strength. All of the strength categories are measured for the subjects excluding for the ones having health problems about their waist.

These measurements are done by using Caldwell Protocol, which says that after a build-up time of about 2 seconds, the subject is required to maintain a steady state maximal exertion for at least 3 seconds and this (average) level is taken as the subject’s strength score. So that we can say that, in general, the isometric strength measurement procedure requires individuals to build-up their muscular exertion slowly over a 4-6 seconds period, without jerking, and maintain the peak exertion for about 3 seconds. This peak exertion (3 sec average) is the isometric strength of the individual.

No external motivation should be provided. A break of at least 30 seconds should be provided between successive exertions if only a few measurements are to be made. It is necessary to increase the rest duration to 2 minutes if about 15 measurements are to be made in one test session. This additional rest is necessary to recover from fatigue generated due to the isometric exertion (Mital and Kumar, 1998). While measuring the strength categories, the effects of some factors like elbow angle, wrist posture, grip span and using dominant or non-dominant side are observed.

The orientation of the arm influences human isometric strength exertion capability. As the arm orientation changes the mechanical advantage also changes, resulting in weaker or stronger strength exertions. Also the wrist orientation is critical in generating isometric torques with non-powered hand tools. Approximately 70% more torque is exerted when wrenches are in the horizontal position than when they are in vertical positions (Mital and Kumar, 1998). Therefore, by using some statistical techniques like ANOVA (Analysis of Variance), the significance of the factors that are mentioned before is tested. . OBJECTIVES The main objective of this study is to investigate the isometric strength or MVC (maximum voluntary contraction) by measuring grip strength, arm strength, leg strength, torso strength and key pinch strength. So that each participant’s strength percentile values in the population for each strength category can be calculated and human biomechanical capabilities and relative variability in human capabilities can be understood by comparing the data collected.

Also the effects of the factors, like grip span, wrist posture or using the dominant side, on each of the strength categories can be examined. 4. METHODS The experimental task consisted of performing isometric handgrip, pinch grip and lifting contractions for the combinations of the levels of wrist posture, grip-span and by using the dominant or non-dominant side in a standing posture. The equipments used are handgrip dynamometer, pinch grip dynamometer and lift platform.

Our lab group conducting this study consists of two female and a male student, which are all right-handed and served as subjects. While measuring the grip strength the combinations of neutral wrist posture with the 5 grip-span settings, vary from 33mm to 85mm, are used. For the 2nd setting the combinations with the wrist flexion and wrist extension are measured too. Then non-dominant side MVC in neutral posture in 3rd setting for males and in 2nd setting for females, and dominant side MVC keeping the wrist in neutral posture and the elbow at 150o angle are measured.

The reason for using the 3rd setting for males and 2nd setting for females is that, on average, 3rd setting is the standard for male hand size, and 2nd setting is the standard for female hand size. After finishing the grip strength measurements, the key pinch strength at neutral arm and wrist posture is measured. After all, by using the lifting platform, MVC for arm, leg and torso strength are measured. While measuring all types of strength categories Caldwell protocol is used and all of the measurements are done as two trials. The data including only the maximum recordings can be seen from the table below: Table 4. : Collected Data for All Group Members for All Strength Categories |Name |Dominant Grip |Non-dominant |Dominant |Arm | | |Strength(MVC) |GS (MVC) |side GS at |Lift | | | | |elbow 150: | | |Female |16. 43 |4. 47 |19. 6 |17 | |Male |37. 86 |6. 69 |44. 81 |19 | The following table combines the data collected from the subjects with the data taken from the population. By looking at it, one can say that for males, with respect to grip strength, our sample mean, 37. 86 is about the 25% tile in strength, which means, Gurkan has more strength than about 25% of the population. And by using the same way, we can say that our female subjects have more strength than 2% of the population on average.

And the information like percentiles for the rest of strength categories can be seen in a same manner from the table below. Table 5. 2: The percentiles of small group and data comparison with the population [pic] Another result from the study is the relationship between grip strength and grip-span and wrist/elbow posture. In order to understand the effects, Analysis of Variance (ANOVA) test is done. Our ANOVA hypothesis is as the following; H0:The group means are not different. H1:The group means are different. For the ANOVA results we can look at the following table to investigate he r-squared values, p-values and f values to understand the effects and relationships.

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According to the results taken; the highest p-valued factor is the wrist posture which has 3 levels of extension, neutral and flexion. Then comes the elbow posture and finally the grip-span. Table 5. 3: R-squared, p-value and f values of factors effecting grip strength |  |  |r-squared |p-value |f | |grip |grip – span |0. 9395 |0. 64 |3. 45 | |strength | | | | | |vs. | | | | | | |wrist posture |0. 8997 |0. 769 |0. 28 | | |elbow posture |0. 9896 |0. 321 |1. 71 |

The quality of the fit is given by the statistical number r-squared. An r-squared of 1. 0 would mean that the model fit the data perfectly, with the line going right through every data point. More realistically, with real data you’d get an r-squared of around 0. 85. Finally in order to support the other techniques we have looked at the result of the Tukey’s test. 5. DISCUSSION The implication of strength measurements in Ergonomic Intervention and the uses and importance of strength data in ergonomic intervention can be explained by the means of the following concepts.

Many industrial activities are performed through human intervention. It is generally accepted that knowledge of what a person can or cannot do under specified circumstances is essential for efficient work design and injury prevention. Human strength recommendations for tool design and work and workspace design have also been receiving considerable attention. Lack of design guidelines and screening procedures can lead to overloading of the muscle-tendon –bone-joint system and, thereby, fatigue and possible consequent injuries. As a matter of fact, Chaffin et al. (1978) have reported that the incidence rate of back injuries ustained on the job increased when the job strength requirements exceeded isometric strengths of the workers. Human strengths of individuals thus form the basis for many design data bases and screening procedures. Therefore we can say that the overall intent of these developments is to reduce injuries and, in the process, maximize industrial productivity. 6. CONCLUSIONS AND RECOMMENDATIONS The results indicate that although there’s not a significant difference, the optimum grip-span setting for the males is the 3rd, for the females is the 2nd setting as it’s been told in the literature.

These findings were supported by the results of tukey’s test, which shows that the maximum strength is achieved with these settings. Also when we look at the R-squared values from the ANOVA test applied to understand the relationship between grip strength and grip-span and wrist/elbow posture, we can see that the effect is not so significant because of the large R-squared values. But if we want to sort them, wrist posture is the most significant, and then grip-span and elbow posture come respectively, where it can be seen easily from the table below: Table 7. : R-squared values for the factors effecting grip strenth |  |  |r-squared | |grip |grip – span |0. 9395 | |strength | | | |vs. | | | |wrist posture |0. 8997 | | |elbow posture |0. 9896 | Although the effect is not significant, the study suggests that grip span of a tool and the posture of wrist and elbow are important factors to be considered.

From this study it can be understood that the isometric strength measurement is inexpensive and flexible. But the major disadvantage of isometric testing is that only one joint angle is tested at a time. If different joint angles need to be tested, the process must be repeated for each angle. Therefore as a further study, ways to test different joint angles one at a time can be studied. REFERENCES Eksioglu, M. , 2006. Optimal work-rest cycles for an isometric intermittent gripping task as a function of force, posture and grip span.

Ergonomics, 49, 180-201. Eksioglu, M. , 2004. Relative optimum grip span as a function of hand anthropometry. International Journal of Industrial Ergonomics, 34, 1-12. Mital, A. , Kumar, S. , 1998. Human muscle strength definitions, measurement, and usage: Part I – Guidelines for the practitioner. International Journal of Industrial Ergonomics, 22, 101-121. Mital, A. , Kumar, S. , 1998. Human muscle strength definitions, measurement, and usage: Part II – The scientific basis (knowledge base) for the guide.

International Journal of Industrial Ergonomics, 22, 123-144. http://en. wikipedia. org/wiki/Confidence_interval http://talkstats. com/showthread. php? t=2460 http://en. wikipedia. org/wiki/Analysis_of_variance http://en. wikipedia. org/wiki/Student’s_t-test http://en. wikipedia. org/wiki/Multiple_comparisons http://www. le. ac. uk/bl/gat/virtualfc/Stats/mult. htm APPENDIX A) Descriptive statistics of the collected sample data (mean, std. dev. , and range only). In calculations, we have considered only the highest values but not all trial values. Table A. Descriptive Statistics of the collected sample data | | |Descriptive Statistics: FEMALE | | | |Variable Mean StDev Variance Range | |FEMALE 16,43 4,47 19,96 17,0 | | | |Descriptive Statistics: MALE | | |Variable Mean StDev Variance Range | |MALE 37,86 6,69 44,81 19,00 | B) Each participant’s strength percentile values in the population for each strength category (pinch strength calculations excluded). Again, we have considered only the highest values but not all trial values. Table B. 1 Grip strength percentile calculations | |Descriptive Statistics: Dicle; Duygu; Gurkan | | | |Variable Q1 Median Q3 IQR | |Dicle 10,00 15,00 17,00 7,00 | |Duygu 15,00 19,00 20,00 5,00 | |Gurkan 33,00 36,00 43,00 10,00 |

Table B. 2 Leg, arm, torso strength percentiles measured for each participant | | |Descriptive Statistics: ARM. M; LEG. F; LEG. M; TORSO. M; TORSO. F;ARM. DYG; ARM. DCL | | | |Variable Q1 Median Q3 IQR | |ARM.

M * 34,300 * * | |LEG. F * 48,40 * * | |LEG. M * 109,90 * * | |TORSO. M * 119,1 * * | |TORSO. F * 53,00 * * | |ARM.

DYG * 15,650 * * | |ARM. DCL * 11,20 * * | C) 95% confidence interval for the true average grip strength, based on the sample data. [pic] C. I. for alpha = 0. 05 For males: (30. 334 , 45. 386) For females: (9. 466 , 23. 394) These intervals say that; in 95 of 100 trials these intervals contain the true average grip strength. D) Grip-strength vs. grip-width and wrist/elbow posture relationships: Table D. 1 ANOVA : Grip-span sets relationship General Linear Model: Value versus Grip; Member | | | |Factor Type Levels Values | |Grip fixed 5 set1; set2; set3; set4; set5 | |Member fixed 3 dicle; duygu; gurkan | | | | | |Analysis of Variance for Value, using Adjusted SS for Tests | | | |Source DF Seq SS Adj SS Adj MS F P | |Grip 4 171,60 171,60 42,90 3,45 0,064 | |Member 2 1374,40 1374,40 687,20 55,20 0,000 | |Error 8 99,60 99,60 12,45 | |Total 14 1645,60 | | | | | |S = 3,52846 R-Sq = 93,95% R-Sq(adj) = 89,41% | Table D. 2 ANOVA : Wrist posture relationship General Linear Model: values versus position; names | | | |Factor Type Levels Values | |position fixed 3 ext; flex; neutral | |names fixed 3 dicle; duygu; gurkan | | | | | |Analysis of Variance for values, using Adjusted SS for Tests | | | |Source DF Seq SS Adj SS Adj MS F P | |position 2 18,67 18,67 9,33 0,28 0,769 | |names 2 1178,00 1178,00 589,00 17,67 0,010 | |Error 4 133,33 133,33 33,33 | |Total 8 1330,00 | | | | | |S = 5,77350 R-Sq = 89,97% R-Sq(adj) = 79,95% | Table D. 3 ANOVA : Elbow position relationship |General Linear Model: numbers versus elbow. ; members | | | |Factor Type Levels Values | |elbow. p fixed 2 ds150; neutral | |members fixed 3 dicle; duygu; gurkan | | | | | |Analysis of Variance for numbers, using Adjusted SS for Tests | | | |Source DF Seq SS Adj SS Adj MS F P |elbow. p 1 6,00 6,00 6,00 1,71 0,321 | |members 2 660,33 660,33 330,17 94,33 0,010 | |Error 2 7,00 7,00 3,50 | |Total 5 673,33 | | | | | |S = 1,87083 R-Sq = 98,96% R-Sq(adj) = 97,40% |

The quality of the fit is given by the statistical number r-squared. An r-squared of 1. 0 would mean that the model fit the data perfectly, with the line going right through every data point. More realistically, with real data you’d get an r-squared of around 0. 85. So that, we can say that the effect of the factors investigated is not significant by looking at the large R-squared values. E) Comparison analysis among the data collected (Analysis of Variance, multiple comparison and t-tests, as necessary). In testing the null hypothesis that the population mean is equal to a specified value ? 0, one uses the statistic [pic] where s is the sample standard deviation of the sample and n is the sample size.

The degrees of freedom used in this test is n ? 1. Also we can use the Multiple Comparison Tests, which are a group of tests that follow on from one or two-factor ANOVA or the Kruskal-Wallis test, but only if significant differences have been found. It would appear that they could be used on their own but because they are not as powerful as ANOVA or Kruskal-Wallis, they can occasionally fail to find differences when the former succeed. They are used for exactly the same reasons that ANOVA and Kruskal-Wallis are used, but provide more information. ANOVA and Kruskal-Wallis can only tell you whether there is a difference between two or more of your groups and not which ones.

We made ANOVA test for the grip-span settings, elbow/wrist posture and dominant/non-dominant side usage. When we look at the R-squared values from the ANOVA results, we fail to reject the null hypothesis; H0: The group means are not different. H1: The group means are different. Because, we have large R-squared values. And also by looking at the F and p-values,which is a small value, we can say that there is not a significant difference between the test groups. There is no need to conduct Tukey test because of failing to reject the null hypothesis, but despite of this situation we have conducted to support our previous results. And again we saw that there is not a significant difference. C25 = set1 subtracted from: | | | |C25 Lower Center Upper ——-+———+———+———+– | |set2 -27,59 5,00 37,59 (————*————) | |set3 -26,26 6,33 38,93 (————-*————) | |set4 -32,59 0,00 32,59 (————*————) | |set5 -35,26 -2,67 29,93 (————*————) | |——-+———+———+———+– | |-25 0 25 50 | In this test, set 1 is compared with the other sets’ average. And as it can be seen the sets are not significantly different, there is only a slight difference, because the range includes “0”. So that there’s chance that different sets give the same results. TUKEY’S TEST FOR SETS: One-way ANOVA: C26 versus C25 | | | |Source DF SS MS F P | |C25 4 172 43 0,29 0,877 | |Error 10 1474 147 | |Total 14 1646 | | | |S = 12,14 R-Sq = 10,43% R-Sq(adj) = 0,00% | | | | | |Individual 95% CIs For Mean Based on | |Pooled StDev | |Level N Mean StDev ——-+———+———+———+– | |set1 3 21,67 12,42 (————*————) | |set2 3 26,67 14,15 (————*————) | |set3 3 28,00 13,75 (————*————) | |set4 3 21,67 10,60 (————*————) | |set5 3 19,00 9,00 (————*————) | |——-+———+———+———+– | |12 24 36 48 | | | |Pooled StDev = 12,14 | | | | |Tukey 95% Simultaneous Confidence Intervals | |

All Pairwise Comparisons among Levels of C25 | | | |Individual confidence level = 99,18% | | | | | |C25 = set1 subtracted from: | | | |C25 Lower Center Upper ——-+———+———+———+– | |set2 -27,59 5,00 37,59 (————*————) | |set3 -26,26 6,33 38,93 (————-*————) | |set4 -32,59 0,00 32,59 (————*————) | |set5 -35,26 -2,67 29,93 (————*————) | |——-+———+———+———+– | |-25 0 25 50 | | | | | |C25 = set2 subtracted from: | | | |C25 Lower Center Upper ——-+———+———+———+– | |set3 -31,26 1,33 33,93 (————-*————) | |set4 -37,59 -5,00 27,59 (————*————) | |set5 -40,26 -7,67 24,93 (————*————) |——-+———+———+———+– | |-25 0 25 50 | | | | | |C25 = set3 subtracted from: | | | |C25 Lower Center Upper ——-+———+———+———+– | |set4 -38,93 -6,33 26,26 (————*————-) | |set5 -41,59 -9,00 23,59 (————*————) | |——-+———+———+———+– | |-25 0 25 50 | | | | | |C25 = set4 subtracted from: | | | |C25 Lower Center Upper ——-+———+———+———+– | |set5 -35,26 -2,67 29,93 (————*————) | |——-+———+———+———+– | | | |-25 0 25 50 | E) CONTINUED Comparison of our data with population data. In order to compare, we should calculate the percentiles; Calculating percentiles for a value relative to a population with known mean (50th %tile) and standard deviation (assuming normal distribution): 1. Calculate z. [pic] z = (37,86 – 40. 5)/3. 84 = -0,69 2. Look up the z value from the table of “cumulative probabilities of the standard normal distribution. ” z = -0,69 is about 0. 25 so the percentile is 25% for the first calculation.

Here is the table of our all computations taken from EXCEL: Table E. 1 Comparison of our data with population data [pic] The following table combines the data collected from the subjects with the data taken from the population. By looking at it, one can say that for males, with respect to grip strength, our sample mean, 37. 86 is about the 25% tile in strength, which means, Gurkan has more strength than about 25% of the population. And by using the same way, we can say that our female subjects have more strength than 2% of the population on average. And the information like percentiles for the rest of strength categories can be seen in a same manner from the table above.

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