RESUMO
This work reports research that investigated the relationship of the strength of contraction of different muscles of the lumbar back to maintain static posture. The paper reports the study of surface electromyogram of the muscles and uses scattering and neural networks on the strength of the EMG measured using root mean square (RMS). The signal is studied before and after the removal of electrocardiogram (ECG) artifact from the signal using a modified independent component analysis technique. The three dimensional scattering plots do not show any observable trends while the neural networks before the removal of ECG does not converge while after removal of ECG the neural network shows a high level of accuracy. The results demonstrate that there is a complex relation between the four EMG and posture and this relationship is revealed only after of ECG from EMG.
RESUMO
Surface electromyogram (SEMG) of the lumbar back muscles is being used for determining posture disorders for people suffering from low back pain. But SEMG of the back has a strong electrocardiogram (ECG) artefact. Research was conducted to determine the difference in the SEMG before and after the removal of ECG artefact from the SEMG recording using gating, subtraction and multi-step independent component analysis (MICA). The paper reports results of experiments conducted on eleven subjects over two days. The results show that removal of ECG artefact from the raw signal can substantially alter the RMS of the signal demonstrating the need for careful filtering of the signal for analysis. The paper also reports of the success of MICA for removing the ECG artefact.
