Identification of femoral-acetabular symptoms using sEMG signals during dynamic contraction
This paper focuses on development of an algorithm that automatically differentiates a Femoro-Acetabular 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 analyse sEMG signals by thirty-eight different wavelet functions (WFs) with 5 decomposition levels of dynamic contractions during the three phases (descending, stationary, and ascending) of a 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).
|Discrete Wavelet Transformation, DWT, EMG Signal Analysis, FAI, Hip Muscles, Non-Stationary Signal, Signal Decomposition, Wavelet Decomposition|
|10th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2017 - Part of 10th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2017|
|Organisation||School of Information Technology|
Tabar, Z.K. (Zahra Karimi), Joslin, C, Lamontagne, M. (Mario), & Mantovani, G. (Giulia). (2017). Identification of femoral-acetabular symptoms using sEMG signals during dynamic contraction. In BIOSIGNALS 2017 - 10th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 10th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2017 (pp. 214–222).