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
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/16491 |
| Date | 11 1900 |
| Creators | Hall, Andrew |
| Contributors | Potvin, James, Kinesiology |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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