Title page for ETD etd-04022008-104730


Type of Document Dissertation
Author Qu, Xingda
Author's Email Address xingdaqu@vt.edu
URN etd-04022008-104730
Title Development and evaluation of postural control models for lifting motions and balance control
Degree PhD
Department Industrial and Systems Engineering
Advisory Committee
Advisor Name Title
Nussbaum, Maury A. Committee Chair
Babski-Reeves, Kari L. Committee Member
Madigan, Michael L. Committee Member
Pérez-Quiñones, Manuel A. Committee Member
Shewchuk, John P. Committee Member
Keywords
  • Postural control
  • lifting
  • balance control
  • fuzzy logic
  • optimal control
Date of Defense 2008-03-28
Availability unrestricted
Abstract
Accurately simulating human motions is a major function of and challenge to digital human models and integrating humans in computer-aided design systems. Numerous successful applications of human motion simulation have already demonstrated their ability to improve occupational efficiency, effectiveness, and safety. In this dissertation, a novel motion simulation model using fuzzy logic control is presented. This model was motivated by the fact that humans use linguistic terms to guide their behaviors while fuzzy logic provides mathematical representations of linguistic terms. Specifically in this model, fuzzy logic was used to specify a neural controller which was generally considered as the part in the postural control system that plans human motions. Fuzzy rules were generated according to certain trends observed from actual human motions. An optimization procedure was performed to specify the parameters of the membership functions by minimizing the differences between the simulated and actual final postures. This research contributed to the field of human movement science by providing a motion simulation model that can accurately predict novel human motions and provide interpretations of potential human motion planning strategies.

Understanding balance control is another research focus in this dissertation. Investigating balance control may aid in preventing unnecessary fall-related incidents and understanding the postural control system. Since human behaviors are generally effective and efficient, balance control models (both two- and three-dimensional) based on an optimal control strategy were developed to aid in better understanding balance control. Specifically, the neural controller was considered as an optimal controller that minimizes a performance index defined by physical quantities relevant to sway. Free model parameters, such as weights of relevant physical quantities and sensory delay time, were determined by an optimization procedure whose objective was to minimize a scalar error between simulated and experimental center-of-pressure (COP) based measures.

Many factors, such as aging, localized muscle fatigue, and external loads, have been found to adversely affect balance control. At the same time, behaviors during upright stance are commonly characterized by COP-based measures. Thus, changes in COP based measures with aging, LMF, and external loads were addressed by using the proposed models, and possible postural control mechanisms were identified by interpreting these changes. Findings from these studies demonstrated that the proposed models were able to accurately simulate human sway behaviors and provide plausible mechanisms regarding how the postural control system works when maintaining upright balance.

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