The aim of the study was to evaluate the effect of surgical reconstruction of anterior cruciate ligament (ACL) on postural stability and responses to lower limb (LL) muscles vibrations.Centre of pressure (CoP) was measured in 17 subjects during stance on firm/foam surface with eyes open/closed and during unilateral vibrations of LL muscles (
m. triceps surae – TS, m. quadriceps femoris – Q, m. quadriceps femoris and hamstrings simultaneously – QH). The measurements were performed: 1) preoperatively, 2) six weeks and 3) three months
after the reconstruction. Decreased postural stability was documented six weeks after the reconstruction compared to preoperative measurement. Three months after the reconstruction significant improvement was observed during stance on foam surface with eyes closed. Preoperatively, altered reactions of LL with ACL lesion compar
ed to intact LL were manifested by slower response in first 3 s of TS vibration and by increased CoP shift in last 5 s of QH vibration. After the reconstruction, we observed slower CoP reaction and decreased
CoP shift during TS vibration of LL with ACL lesion compared to
preoperative level. Posturography during quiet stance and during TS vibration reliably detect postural changes due to ACL reconstruction and can be potentially useful in clinical practice.
Train-induced vibration prediction in multi-story buildings can effectively provide the effect of vibrations on buildings. With the results of prediction, the corresponding measures can be used to reduce the influence of the vibrations. To accurately predict the vibrations induced by train in multi-story buildings, support vector machine (SVM) is used in this paper. Since the parameters in SVM are very vital for the prediction accuracy, shuffled frog-leaping algorithm (SFLA) is used to optimize the parameters for SVM. The proposed model is evaluated with the data from field experiments. The results show SFLA can effectively provide better parameter values for SVM and the SVM models outperform a better performance than artificial neural network (ANN) for train-induced vibration prediction