A study was made to investigate the relative effectiveness of two methods of training on the development of arm strength. The purposes of the study included the following: 1. To determine functional arm strength of male students enrolled in physical education classes selected for the study. 2. To conduct specific exercise programs in each of the selected classes. 3. To determine the relative effectiveness of the selected programs in developing arm strength.

Ergonomics software programs often use an independent axis approach (IAA) to calculate resultant shoulder strength to predict manual arm strength (MAS). The IAA treats strength about each joint axis (joint axis strengths: JAS) in the arm as independent motors, which all combine to complete an exertion. However, this form of modeling is not a true physiological representation of how the shoulder/arm function. The weighted average approach (WAA) was proposed, which combines the axes by weighting each strength based on its relative contribution to the resultant moment vector. The primary purpose of this thesis was to test the IAA using participant-specific JAS values, such that it afforded the IAA the best opportunity to predict MAS accurately. The secondary purpose was to test the WAA, to determine if it was a viable replacement for the IAA. Fifteen university age females completed two data collections. One tested the eight different JASs for the shoulder and elbow, and the other tested participant’s MASs in four hand locations and six exertion directions. The JAS force data, and postural kinematic data (from the MAS collection), were inputs into two models, which completed the MAS predictions. A 4 x 6 x 3 repeated measures ANOVA revealed a significant three-way interaction between hand location, exertion direction, and method of MAS estimation (p<0.0001) on MAS. The most important finding of the thesis was that both the IAA and WAA predictions were significantly different than the MAS values. The IAA and WAA explained only 17.9% & 19.1% of the variance with RMS errors of 74.5 N & 73.4 N, respectively. This indicated that ergonomics software programs, using the IAA, should not be used to make arm strength predictions by ergonomists, and that WAA was not a viable replacement for the IAA. / Thesis / Master of Science in Kinesiology

The primary purpose of this thesis was to develop, validate and implement a novel ergonomics tool for manual arm strength (MAS) prediction. In Chapter 2, an empirical study was conducted to: 1) fill in gaps in our MAS database, and 2) examine the relationships between MAS and shoulder/elbow moments, to help identify important sources of variance for future predictive modeling attempts. Chapter 3 focused on the evaluation of artificial neural network (ANN) and traditional multiple regression approaches for MAS prediction, and revealed that ANNs provided a more accurate and generalizable prediction of MAS for our specific dataset. Chapter 4 drew on the data and findings of Chapters 2 & 3, and described the development of the ‘Arm Force Field’ (AFF) method for MAS prediction. The AFF method can be used to predict the MAS for any percentage of the population, given only the simple inputs of force vector direction, hand location (relative to the right shoulder), and torso orientation. In Chapter 5, a theoretical examination of the relative changes in wrist strength, due to interacting forearm and wrist postures, was conducted. That study resulted in a set of regression equations that can be used to predict wrist strength correction factors in complex wrist and forearm postures, allowing for more accurate estimations of the limiting joint once the MAS is calculated. An example of the AFF method’s implementation is provided and discussed in Chapter 6. The four studies, presented in this thesis, add to the current knowledge related to strength prediction in ergonomics, and the AFF method has the potential to be easily integrated within digital human models, for more valid estimates of manual force capabilities for the population. / Dissertation / Doctor of Philosophy (PhD)

<p>The primary purpose of this thesis was to measure arm strengths, in combinations of exertion directions, and to evaluate the importance of knowing the precise posture of the arm and specific joint locations in 3D space when predicting female arm strength. A stepwise multiple regression approach was utilized in the prediction of female arm strengths, using kinematic measures of hand location, arm posture and 26-force directions from 17 subjects and 8 hand locations as inputs. When including measures of arm posture, the regression model was indeed improved, explaining 75.4% of the variance, with an RMS error of 9.1 N, compared to an explained variance of 67.3% and an RMS error of 10.5 N without those postural variables. A comparison was also made between the empirical strength data from this thesis and the outputs from the University of Michigan’s Center for Ergonomics 3-Dimensional Static Strength Prediction Program (3DSSPP) software. A poor correlation (R-square = 0.305) and high RMS error (39 N) was found, indicating a definite need for further evaluation of the 3DSSPP package, as it is one of the most commonly used ergonomic tools in industry. <strong> </strong></p> / Master of Science (MSc)

Introduction: External support behaviours, which include leaning (supporting with the non-task hand) or bracing (supporting with the body), are frequently employed by workers in manufacturing settings. However, current ergonomic assessment tools are limited by our limited understanding of these behaviours. Recent studies have investigated these behaviours, however, the designs of these studies are limited in their applicability to real-world scenarios. The purpose of this study was to assess how different task parameters affect the prediction of external support behaviours, as well as the effect of support on task hand, and body, kinematics and kinetics, in a minimally constrained experimental design. Methods: Female participants (n = 18) performed a series of one-handed maximal exertions (in the six orthogonal directions), and one precision task, in four hand Locations. Trials either featured support (as chosen by the participant), or no support. Results & Discussion: Three logistic regression models were developed, with inputs from individual and task characteristics, and they correctly predicted the occurrence of leaning, bracing, or simultaneous leaning and bracing, 74-86% of the time. Leaning and/or bracing were found to provide: 1) oppositional forces to increase task hand force generation, 2) balance, by countering destabilizing moments about the feet, and 3) a reduction in moment arm of the task hand force, with respect to the upper body joints, by bringing the shoulder closer to the task hand. Participants were able to exert 64.8% more force at the task hand as a result of support. Leaning hand placement depended on the task force direction and location. However, the positioning of the leaning hand varied very little. Finally, the precision condition showed that fine motor demands may also affect external support choice. / Thesis / Master of Science in Kinesiology