This thesis focuses on development of an algorithm that automatically identifies a Femoroacetabular Impingement (FAI) patient from a healthy control person by comparing their surface electromyography (sEMG) signal recorded from Gluteus Maximus (GMax), Tensor Fasciae Latae (TFL), and Rectus Femoris (RF) muscles in the hip area. A discrete wavelet transform (DWT) method was used to analyze sEMG signals by thirty eight different wavelet functions (WFs) with 5 decomposition levels of dynamic

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... tions during the three phases (descending, stationary, and ascending) of squat task. The Bior3.9 WF was selected as it provided higher amount of energy for most of the subjects and then the wavelet power spectrum was computed for healthy control and FAI groups. The results show that the RF muscle is more active in the ascending phase than the descending phase for FAI subjects, whereas it is more active in the descending phase for healthy control. An independent sample t-test was used to check the activities of muscle in both groups. The results demonstrate no significant difference for GMax (p=0.7477) and TFL (P=0.4997) muscles, while there is a significant difference for RF muscle (p=0.0670).