Assessment on Stationarity of EMG Signals with Different Windows Size During Isotonic Contractions

Abstract

Abstract:In order to analyse surface electromyography (EMG) signals, it is necessary to extract the features based on a time or frequency domain. These approaches are based on the mathematical assumption of signal stationarity. Stationarity of EMG signals is thoroughly examined, especially in isotonic contractions. According to research, conflicting results have been identified depending on varying window sizes. Therefore, in this study, the authors endeavoured to determine the suitable window size to analyse EMG signals during isotonic contractions utilising stationary tests, reverse arrangement (RA), and modified reverse arrangement (MRA). There were slight differences in the average percentages of signal stationarity for RA and MRA tests in 100 ms, 500 ms, and 1000 ms window sizes. However, there was none for the 200 ms window size. On average, a window size of 200 ms provided stationary information with 88.57% of EMG signals compared to other window sizes. This study also recommended the MRA test to determine EMG signals stationarity for future studies, as the performances were better in comparison to RA tests. However, the following recommendation is only valid for window sizes greater than 200 ms. For a real-time application, the size of the analysis window together with the processing time should be less than 300 ms and a window size of 200 ms is applicable for isotonic contractions.

Changes in lower limb muscle synchronisation during walking on high‐heeled shoes

“…Common oscillatory drive to a muscle pair was quantified by EMG-EMG coherence [20], which was calculated using Welch's periodogram method with a Hamming window of 2048 samples and overlap of 1024 samples [20,21]. The coherence was calculated using the following well-known equation [20,22]:…”

“…Common oscillatory drive to a muscle pair was quantified by EMG‐EMG coherence [20], which was calculated using Welch's periodogram method with a Hamming window of 2048 samples and overlap of 1024 samples [20, 21]. The coherence was calculated using the following well‐known equation [20, 22]: where is cross‐spectra and , are the auto‐spectra of a ( t ) and b ( t ) which are the EMG signals from one muscle pair at a time (RF‐VL, RF‐VM and RF‐ST).…”

“…During this synchronisation, common neural inputs are sent to these muscles from the central nervous system (CNS). Common neural oscillations from the CNS are observed in beta band (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) [10] during human walking. According to previous research [8,11], the common drive is present only in a muscle pair that has similar functions to maintain stability around a common joint during walking.…”

“…The mean and standard deviation of beta band coherence values of all muscle pairs for different HHS (during walking) are given inTable 1. Average beta band(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) coherence values for RF-VL, RF-VM and RF-ST muscle pairs, during walking on low, medium and high heels shoes is shown in…”

An Ensemble Classifier for Finger Movement Recognition using EMG Signals

“…In this study, overlapping windowing is applied. A window length of 150 ms to 250 ms was recommended in the studies with the EMG signals [19,20]. The window length was chosen as 200 ms.…”