Despite a recent focus on understanding cumulative load, researchers still prefer to analyze the data using mean and peak values. At the same time, technological advancements have provided biomechanists with methods of collecting large amounts of data pertaining to joint loading. Waveform analysis offers another option that will become increasingly relevant, as wireless data collection devices become common place and provide access to waveforms from many hours of recording. The overall objectives of this research were to demonstrate some of the limits of current methods of biomechanical analysis, and introduce an alternative, and secondly, to propose a wireless system for use in field-based studies.
An exploratory study using Functional Data Analysis (FDA) was completed on experimental lifting data. The results demonstrated that FDA can elucidate subtle differences in the curve shape outside of the peak areas typically used for statistical analysis that were attributed to fatigue. These findings support the need for a better understanding of how workers change their movement strategies as time progresses throughout the length of the workshift.
To achieve this type of knowledge, a wireless data collection device utilizing inertial motion sensors (IMS) was introduced and validated for field use in the remaining three chapters. The IMS units in conjunction with an anthropometric model were tested against a traditional link segment model recorded in a gold-standard, video system. Testing that occurred in the entire reach space volume yielded errors as low as 5% for the lumbar moment, but errors also greatly exceeded 50% RMS error in some cases. Three hand switch alternatives were tested for their potential to provide external hand force timing and duration information to the link segment model, but none were found to be perfectly suitable in the current configuration.
In conclusion, a wireless system based on IMS units has the potential to provide long-term data collection, but the development of the calibration routines and complexity of the underlying model must be improved. FDA was shown to have good potential for identifying subtle differences in curve shapes, and may become useful when long-term field-based data are readily available with the proposed IMS system. / Thesis (Ph.D, Kinesiology & Health Studies) -- Queen's University, 2009-04-21 09:01:22.968
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OKQ.1974/1751 |
| Date | 21 April 2009 |
| Creators | Godwin, ALISON |
| Contributors | Queen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.)) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | 4221813 bytes, application/pdf |
| Rights | This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner. |
| Relation | Canadian theses |
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