Title page for ETD etd-101399-155919

Type of Document Master's Thesis
Author Perez, Miguel A.
Author's Email Address mperez@vt.edu
URN etd-101399-155919
Title Empirical Evaluation of Models Used to Predict Torso Muscle Recruitment Patterns
Degree Master of Science
Department Industrial and Systems Engineering
Advisory Committee
Advisor Name Title
Nussbaum, Maury A. Committee Chair
Kleiner, Brian M. Committee Member
Raschke, Ulrich Committee Member
  • muscle modeling
  • lumbar spine
  • optimization
  • neural networks
Date of Defense 1999-09-24
Availability unrestricted
For years, the human back has puzzled researchers with the complex behaviors it presents. Principally, the internal forces produced by back muscles have not been determined accurately. Two different approaches have historically been taken to predict muscle forces. The first relies on electromyography (EMG), while the second attempts to predict muscle responses using mathematical models. Three such predictive models are compared here. The models are Sum of Cubed Intensities, Artificial Neural Networks, and Distributed Moment Histogram. These three models were adapted to run using recently published descriptions of the lower back anatomy. To evaluate their effectiveness, the models were compared in terms of their fit to a muscle activation database including 14 different muscles. The database was collected as part of this experiment, and included 8 participants (4 male and 4 female) with similar height and weight. The participants resisted loads applied to their torso via a harness. Results showed the models performed poorly (average R2's in the 0.40's), indicating that further improvements are needed in our current low back muscle activation modeling techniques. Considerable discrepancies were found between internal moments (at L3/L4) determined empirically and measured with a force plate, indicating that the maximum muscle stress selected and/or the anatomy used were faulty. The activation pattern database collected also fills a gap in the literature by considering static loading patterns that had not been systematically varied before.
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