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dc.contributor.authorAstin, Angela DiDomenicoen_US
dc.date.accessioned2014-03-14T20:30:33Z
dc.date.available2014-03-14T20:30:33Z
dc.date.issued1999-12-16en_US
dc.identifier.otheretd-01132000-12570058en_US
dc.identifier.urihttp://hdl.handle.net/10919/30923
dc.description.abstractHand and finger force data are used in many settings, including industrial design and indicating progress during rehabilitation. The application of appropriate work design principles, during the design of tools and workstations that involve the use of the hand and fingers, may minimize upper extremity injuries within the workplace. Determination and integration of force capabilities and requirements is an essential component of this process. Available data in the literature has focused primarily on whole-hand or multi-digit pinch exertions. The present study compiled and examined maximal forces exerted by the fingers in a variety of couplings to both enhance and supplement available data. This data was used to determine whether finger strength could be predicted from other strength measures and anthropometry. In addition, this study examined whether exerted finger forces could be estimated using surface electromyography obtained from standardized forearm locations. Such processes are of utility when designing and evaluating hand tools and human-machine interfaces involving finger intensive tasks, since the integration of finger force capabilities and task requirements are necessary to reduce the risk of injury to the upper limbs.

Forces were measured using strain gauge transducers, and a modification of standard protocols was followed to obtain consistent and applicable data. Correlations within and among maximum finger forces, whole-hand grip force, and anthropometric measures were examined. Multiple regression models were developed to determine the feasibility of predicting of finger strength in various finger couplings from more accessible measures. After examining a wide variety of such mathematical models, the results suggest that finger strength can be predicted from easily obtained measures with only moderate accuracy (R2-adj: 0.45 - 0.64; standard error: 11.95N - 18.88N). Such models, however, begin to overcome the limitations of direct finger strength measurements of individuals.

Surface electrodes were used to record electromyographic signals collected from three standardized electrode sites on the forearm. Multiple linear regression models were generated to predict finger force levels with the three normalized electromographic measures as predictor variables. The results suggest that standardized procedures for obtaining EMG data and simple linear models can be used to accurately predict finger forces (R2-adj: 0.77 - 0.88; standard error: 9.21N - 12.42N) during controlled maximal exertions. However, further work is needed to determine if the models can be generalized to more complex tasks.

en_US
dc.publisherVirginia Techen_US
dc.relation.haspartthesis_astin.pdfen_US
dc.rightsI hereby grant to Virginia Tech or its agents the right to archive and to make available my thesis or dissertation in whole or in part in the University Libraries in all forms of media, now or hereafter known. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation.en_US
dc.subjectpinchesen_US
dc.subjectpredictionen_US
dc.subjectelectromyographyen_US
dc.subjectfinger strengthen_US
dc.titleFinger force capability: measurement and prediction using anthropometric and myoelectric measuresen_US
dc.typeThesisen_US
dc.contributor.departmentIndustrial and Systems Engineeringen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
dc.contributor.committeechairNussbaum, Maury A.en_US
dc.contributor.committeememberKroemer, Karl H. E.en_US
dc.contributor.committeememberWojcik, Laura A.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-01132000-12570058/en_US
dc.date.sdate2000-01-13en_US
dc.date.rdate2001-01-14
dc.date.adate2000-01-14en_US


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