Periacetabular osteotomy (PAO) is a complex surgical procedure to restore acetabular coverage in the dysplastic hip, and the amount of acetabular rotation during PAO plays a key role. Using computational simulations, this study assessed the optimal direction and amount of the acetabular rotation in three dimensions for a patient undergoing PAO. Anatomy-specific finite element (FE) models of the hip were constructed based on clinical CT images. The calculated acetabular rotation during PAO were 9.7°, 18°, and 4.3° in sagittal, coronal, and transverse planes, respectively. Based on the actual acetabular rotations, twelve postoperative FE models were generated. An optimal position was found by gradually varying the amount of the acetabular rotations in each anatomical plane. The coronal plane was found to be the principal rotational plane, which showed the strongest effects on joint contact pressure compared to other planes. It is suggested that rotation in the coronal plane of the osteotomized acetabulum is one of the primary surgical parameters to achieve the optimal clinical outcome for a given patient.
Many researchers have tried to detect the falling and to reduce the injury associated with falling. Normally the method of detection of a loss of balance is more efficient than that of a compensatory motion in order to predict the falling. The detection algorithm of the loss of balance was composed of three main parts: parts of processing of measured data, construction of an internal model and detection of the loss of balance. The internal model represented a simple dynamic motion balancing with two rear legs of a four-legged chair and was a simplified model of a central nervous system of a person. The internal model was defined by the experimental data obtained within a fixed time interval, and was applied to the detecting algorithm to the end of the experiment without being changed. The balancing motion controlled by the human brain was improved in process of time because of the experience accruing to the brain from controlling sensory organs. In this study a reconstruction method of the internal model was used in order to improve the success rate and the detecting time of the algorithm and was changed with time the same as the brain did. When using the reconstruction method, the success rate and the detecting time were 95 % and 0.729 sec, respectively and those results were improved by about 7.6 % and 0.25 sec in comparison to the results of the paper of Ahmed and Ashton-Miller. The results showed that the proposed reconstruction method of the internal model was efficient to improve the detecting performance of the algorithm.
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