Use of Statistical Mechanics Methods to Assess the Effects of Localized muscle fatigue on Stability during Upright Stance

Human postural control is a complex process, but that is critical to understand in order to reduce the prevalence of occupational falls. Localized muscle fatigue (LMF), altered sensory input, and inter-individual differences (e.g. age and gender) have been shown to influence postural control, and numerous methods have been developed in order to quantify such effects. Recently, methods based on statistical mechanics have become popular, and when applied to center of pressure (COP) data, appear to provide new information regarding the postural control system. This study addresses in particular the stabilogram diffusion and Hurst exponent methods. An existing dataset was employed, in which sway during quiet stance was measured under different visual and surface compliance conditions, among both genders and different age groups, as well as before and after induction of localized muscle fatigue at the ankle, knee, torso, and shoulder. The stabilogram diffusion method determines both short-term and long-term diffusion coefficients, which correspond to open- and closed-loop control of posture, respectively. To do so, a "critical point" (or critical time interval) needs to be determined to distinguish between the two diffusion regions. Several limitations are inherent in existing methods to determine this critical point. To address this, a new algorithm was developed, based on a wavelet transform of COP data. The new algorithm is able to detect local maxima over specified frequency bands within COP data; therefore it can identify postural control mechanisms correspondent to those frequency bands. Results showed that older adults had smaller critical time intervals, and indicating that sway control of older adults was essentially different from young adults. Diffusion coefficients show that among young adults, torso LMF significantly compromised sway stability. In contrast, older adults appeared more resistance to LMF. Similar to earlier work, vision was found to play a crucial role in maintaining sway stability, and that stability was worse under eyes-closed (EC) than eyes-opened (EO) conditions. It was also found that the short-term Hurst exponent was not successful at detecting the effects of LMF on sway stability, likely because of a small sample size. The new critical point identification algorithm was verified to have better sensitivity and reliability than the traditional approach. The new algorithm can be used in future work to aid in the assessment of postural control and the mechanisms underlying this control.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Industrial and Systems Engineering

Department dc:contributor.department

Industrial and Systems Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2007

Author dc:creator

• Zhang, Hongbo

Chair dc:contributor.committeechair

• Nussbaum, Maury A.

Committee members dc:contributor.committeemember

• Madigan, Michael L.
• Babski-Reeves, Kari L.

• Wavelet Transform
• Stabilogram Diffusion Algorithm
• Hurst Exponent
• Postural Control
• Localized Muscle Fatigue
• Proprioception
• Balance

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

etd-01102007-153932

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/30888