Browsing by Author "Gary Mirka, Committee Chair"

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    Effects of Trunk Extensor Muscle Fatigue on Trunk Proprioception and Biomechanics.
    (2006-12-18) Kelaher, Dan; Simon Roe, Committee Member; Sam Moon, Committee Member; Carolyn Sommerich, Committee Member; Gary Mirka, Committee Chair
    Localized muscle fatigue of the low back musculature is common in workers who perform repetitive lifting and manual materials handling tasks. A significant amount of research has been performed on the basic physiology and biomechanics of localized muscle fatigue during heavy physical exertions. Far fewer studies have focused on localized muscle fatigue of gradual onset which is more consistent with that seen in occupational environments. Also, none of this previous research has developed a model that evaluates the important interactions of the various biomechanical responses. The current research considered the effects of gradual onset trunk extensor muscle fatigue on the interactive effects of trunk proprioception and biomechanics. Localized muscle fatigue was generated in six human subjects as they performed sets of 30 sagittally-symmetric isokinetic trunk extension exertions wherein the required extension moments were gradually increased as the experiment progressed. Interspersed among these sets of fatiguing exertions were a series of testing "modules" that evaluated various dimensions of the response to the fatiguing exertions. These modules included a trunk repositioning module, a trunk extension force replication module, a trunk extension reaction time module, a three dimensional lifting task module, an EMG-based, sagittally symmetric trunk extension module and an EMG-based sagittally asymmetric trunk extension module. These six modules were performed before and after the fatiguing exertions to quantify the fatigue-induced changes in each of these measures independently. Further, each of these tasks were performed multiple times in each module to gain an appreciation for the variability of the response measure. (For comparison, a group of three anthropometry-matched control subjects performed the six modules without performing the fatiguing exertions.) Finally, a correlation analysis of the inter-relationships between these responses was conducted to gain a more complete understanding of the inter-relationships among these responses. Dependent measures considered in these analyses included measures of average performance ("accuracy") and variability of performance ("precision") while the independent variable was time into the experiment (cumulative fatigue.) The results of this correlation analysis were then compared with the expectation of the theoretical model to evaluate the fundamental concepts that the model put forward. The results for the individual tests agreed with previous research performed on the low back extensor muscle fatigue. In general, the following trends were seen as a function of time into the experiment: trunk extensor muscle onset time increased, trunk extensor NEMG increased, trunk repositioning accuracy decreased, force replication accuracy decreased, the lumbar angles in all three planes during the kinematics test decreased at the start of the lift, and peak angular velocities in all three planes increased in the kinematics test. There were very few significant effects of the time into the experiment on the precision or variability of the various measures. The proprioceptive measures of force replication and trunk angle reproduction in all three planes worsened with fatigue. There was an average increase of 37% in the average absolute error in trunk repositioning test while there was almost a 53% increase in average force replication absolute error. Peak lumbar angular velocities also increased with fatigue 17% above the pre-fatigued values, on average. The data provides support for a proposed model which includes proprioceptive and biomechanical components linked together. The data strongly supported the kinematic links in the model with a number of significant correlations among the different variables above 0.50. However, the data did not support the kinetic links as there were no significant correlation coefficients above 0.50 or below −0.50. Future research that further integrates proprioception and biomechanical measures are needed. Industrial engineers and ergonomists are recommended to focus on the effects of gradual onset muscle fatigue and the posture and motion parameters seen during repeated trunk exertions in industry.
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    Intelligent System Identification Applied to the Biomechanical Response of the Human Trunk during Sudden Loading
    (2002-11-25) Lawrence, Brad Michael; Carolyn Sommerich, Committee Member; David Kaber, Committee Member; Gary Mirka, Committee Chair; Gregory Buckner, Committee Co-Chair
    Current techniques in biomechanics are not sufficient for modeling sudden loads. Modeling techniques used in intelligent system identification, where self-adapting basis functions identify time-varying model parameters, may provide a superior method for describing human trunk dynamics during sudden loading. By deriving time-varying system dynamics, more insight is gained into how the trunk responds to sudden loads. Six males were subjected to sudden loads with varying conditions of expectation, fatigue, and training. Electromyographical and trunk motion data were recorded, and processed using a novel system identification algorithm to yield dependent measures of peak and average trunk stiffness, peak muscular torque, work, and impulse. This system identification model yielded valid, accurate, and robust results with minor experimental cost. The model calculated physiologically valid stiffness magnitudes ranging from an average stiffness mean of 532 Nm/rad (standard deviation = 306 Nm/rad) to a peak stiffness mean of 1314 Nm/rad (standard deviation = 814 Nm/rad). The model root mean square accurately predicted empirical angular displacement at a value of 0.1˚. Across varying model inputs, dependent variable output magnitudes differed by less than 5%. On average, the model required 4000 iterations per trial (less than 5 minutes per trial) to converge and obtain dependent variable output. Model output indicated that expectation significantly increased peak and average stiffness (p<0.0001) by 70% and 113% respectively, and significantly decreased peak torque, work, and impulse magnitudes (p<0.0001) by 36%, 63%, and 56% respectively; training significantly decreased peak torque and work magnitudes (p<0.05) by 25% and 36% respectively; the interaction of expected loads with training significantly increased peak and average stiffness (p<0.05) by 17% and 20% respectively, and decreased impulse magnitude (p<0.05) by 53%; and the interaction of unexpected loads with fatigue significantly decreased work and impulse magnitudes (p<0.05) by 23% and 17% respectively. These results led to workplace recommendations that could potentially reduce low back pain. This intelligent system identification approach is easy to implement and has potential application to address a multitude of research questions in the field of biomechanics.