Browsing by Author "Lee, Minhyung"

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    Analysis of Lumbar Spine Kinematics during Trunk Flexion and Extension Motions
    Lee, Minhyung (Virginia Tech, 2006-01-20)
    The effectiveness of exercise has been increasingly studied as exercise has been popular for the improvement of physical performance and rehabilitation of lumbar spine. A variety of exercises have been used to reduce back pain or spinal degeneration. However, there are no studies to determine effects of exercise on lumbar spine kinematics, including lumbar-pelvic coordination and instantaneous axis of rotation. The current study aimed to examine these lumbar spine kinematical changes due to exercise and therapy. We hypothesized that exercise and therapy will affect the changes of lumbar spine kinematics. Lumbar-Pelvic motions were recorded from 86 healthy subjects while performing lifting and lowering tasks of 10% and 25% of body weight. The influence of exercise was quantified from coefficients of curve-fitting for pelvic and lumbar angles. There was a significant difference (p<0.05) for the range of lumbar motion (distribution, D) between the control group and the cardiovascular exercise group after 12-week program. However, there was no significance for lumbar-pelvic coordination, C. A second study was performed to investigate the changes of instantaneous axis of rotation (IAR) at which trunk angle reached 25º. Results indicated that a superior-inferior location of IAR was significantly (p<0.05) modified by the cardiovascular exercise after 12 weeks, but there was no significant effectiveness of the physical therapy exercise. Finding of lumbar spine kinematics during lifting and lowering a weight which are the most popular manual handling activities may provide great understanding of the exercise effectiveness. Future studies are recommended to assess whether the changes of lumbar spine kinematics lead to the decrease instances of lumbar spine injuries or low back pain.
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    Biomechanical adaptations of human gait due to external loads
    Lee, Minhyung (Virginia Tech, 2008-08-01)
    Gait is the method of human locomotion using limbs. Recently, the analysis of human motion, specifically human gait, has received a large amount of research attention. Human gait can contain a wide variety of information that can be used in biometrics, disease diagnosis, injury rehabilitation, and load determination. In this dissertation, the development of a model-based gait analysis technique to classify external loads is presented. Specifically, the effects of external loads on gait are quantified and these effects are then used to classify whether an individual gait pattern is the result of carrying an external load or not. First of all, the reliability of using continuous relative phase as a metric to determine loading condition is quantified by intra-class correlation coefficients (ICC) and the number of required trials is computed. The ICC(2, 1) values showed moderate reliability and 3 trials are sufficient to determine lower body kinematics under two external load conditions. Then, the work was conducted to provide the baseline separability of load carriage conditions into loaded and unloaded categories using several lower body kinematic parameters. Satisfactory classification of subjects into the correct loading condition was achieved by resorting to linear discriminant analysis (LDA). The baseline performance from 4 subjects who were not included in training data sets shows that the use of LDA provides an 88.9% correct classification over two loaded and unloaded walking conditions. Extra weights, however, can be in the form of an external load carried by an individual or excessive body weight carried by an overweight individual. The study now attempts to define the differences in lower body gait patterns caused by either external load carriage, excessive body weight, or a combination of both. It was found significant gait differences due to external load carriage and excessive body weight. Principal Component Analysis (PCA) was also used to analyze the lower body gait patterns for four loading conditions: normal weight unloaded, normal weight loaded, overweight unloaded and overweight loaded. PCA has been shown to be a powerful tool for analyzing complex gait data. In this analysis, it is shown that in order to quantify the effects of external loads for both normal weight and overweight subjects, only two principal components (PCs) are needed. The results in this dissertation suggest that there are gait pattern changes due to external loads, and LDA could be applied successfully to classify the gait patterns with an unknown load condition. Both load carriage and excessive body weight affect lower body kinematics, but it is proved that they are not the same loading conditions. Methods in the current work also give a potential for new medical and clinical ways of investigating gait effects in osteoarthritis patients and/or obese people.