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)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/18687 |
| Date | 06 1900 |
| Creators | La Delfa, Nicholas Joseph |
| Contributors | Potvin, James Robert, Kinesiology |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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