A biomechanical analysis of shoulder loading and effort during load transfer tasks.

Show simple item record

dc.contributor.author Dickerson, Clark Rutherford
dc.contributor.advisor Chaffin, Don B.
dc.contributor.advisor Hughes, Richard E.
dc.date.accessioned 2016-08-30T15:44:16Z
dc.date.available 2016-08-30T15:44:16Z
dc.date.issued 2005
dc.identifier.uri http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:3163787
dc.identifier.uri http://hdl.handle.net/2027.42/124776
dc.description.abstract Occupational tasks include external loads and dynamic postures that generate internal forces on the musculoskeletal system. This loading is often characterized through the calculation of static joint torques. This addresses neither kinetic motion effects nor the loading and stress in individual muscles during tasks. This dissertation attempts to improve the quantification of shoulder loading and effort perception for occupational reaching tasks. A series of mathematical models are created that use motion, task, and subject-specific data streams to calculate dynamic shoulder joint torques, predict instantaneous muscle force profiles for thirty-eight muscle units, and estimate the effort level perceived in the shoulder. Additionally, a visual representation of the internal geometry of the shoulder is created. It includes 3-dimensional shoulder bone orientations and muscle elements that emulate physiological paths. An exploratory (eight subject) empirical evaluation of the muscle model outputs demonstrates that predictions were significantly positively correlated with electromyographic data for the primary active muscles defined as agonists (average of r = 0.53 and 0.63 for deltoid and infraspinatus, respectively). The model performs somewhat less accurately in predicting activity of muscles not strictly required to resist external joint torques. This is due to a combination of the complexity of the glenohumeral stability maintenance and limitations associated with applying a monotonically increasing optimization cost function. Effort perception is predicted more accurately using shoulder torque loading, combined with subject and task characteristics (r<super>2</super> = 0.74), than with models based on either muscle force predictions or experimental EMG data (r<super>2</super> = 0.67 and 0.64, respectively). Although imperfect in predicting some muscle forces, the created model generates data streams that had been inaccessible to practicing ergonomists. The prospective ability to estimate force and stress activity in the prime movers for a planned task and to predict a worker's psychophysical response could positively influence job design. Finally, these models are implemented in a modular format in accessible software for potential future inclusion in job analysis software. This enables work analyses of shoulder activity of a previously unavailable level of fidelity.
dc.format.extent 206 p.
dc.language English
dc.language.iso EN
dc.subject Analysis
dc.subject Biomechanical
dc.subject Effort
dc.subject Joint Torque
dc.subject Load
dc.subject Reaching Tasks
dc.subject Shoulder Loading
dc.subject Transfer
dc.title A biomechanical analysis of shoulder loading and effort during load transfer tasks.
dc.type Thesis
dc.description.thesisdegreename Ph.D.
dc.description.thesisdegreediscipline Applied Sciences
dc.description.thesisdegreediscipline Biomedical engineering
dc.description.thesisdegreediscipline Health and Environmental Sciences
dc.description.thesisdegreediscipline Kinesiology
dc.description.thesisdegreediscipline Occupational safety
dc.description.thesisdegreegrantor University of Michigan, Horace H. Rackham School of Graduate Studies
dc.description.bitstreamurl http://deepblue.lib.umich.edu/bitstream/2027.42/124776/2/3163787.pdf
dc.owningcollname Dissertations and Theses (Ph.D. and Master's)
 Show simple item record

This item appears in the following Collection(s)


Search Deep Blue

Advanced Search

Browse by

My Account

Information

Available Now


MLibrary logo