Teager–Kaiser Energy Operation of Surface EMG Improves Muscle Activity Onset Detection

Abstract

This study presents a novel method for detection of the onset time of muscle activity using surface electromyogram (EMG) signals. The method takes advantage of the nonlinear properties of the Teager-Kaiser energy (TKE) operator, which simultaneously considers the amplitude and instantaneous frequency of the surface EMG, and therefore increases the prospects of muscle activity detection. To detect the onset time of muscle activity, the surface EMG signal was first processed by the TKE operator to highlight motor unit activities of the muscle. Then a robust threshold-based algorithm was developed in the TKE domain to locate the onset of muscle activity. The validity of the proposed method was illustrated using various surface EMG simulations as well as experimental surface EMG recordings.

Muscular strategy shift in human running: dependence of running speed on hip and ankle muscle performance

“…Electrodes were placed according to previously published guidelines (Hermans et al, 2000), and all signals were checked for clarity and strength of signal during isolated limb movements. EMG onset and offset times were determined by applying a Teager-Kaiser energy (TKE) filter to the raw EMG signal (Li et al, 2007;Solnik et al, 2010). Running sandals (Nike Straprunner IV, Nike, Beaverton, OR, USA) rather than traditional shoes or spikes were worn by the subjects so that markers could be placed directly onto the foot.…”

The time course of visual influences in letter recognition

“…Therefore, letter naming was completely digitized at 22040 Hz on a single channel, in order to check for response accuracies and to determine RTs offline. On each digitized trial, we first employed a semi-automatic procedure by applying an algorithm based on the Teager-Kaiser operator, which detects sudden variations of energy in the acoustic signal (Li, Zhou, & Aruin, 2007; see also Riès, Legou, Burle, Alario, & Malfait, 2012). Second, the results of this operator were convoluted through a moving average window, acting as a lowpass filter discarding artifactual noises unrelated to the participant's answer.…”

Embedded Wearable Integrating Real-Time Processing of Electromyography Signals

“…In fact, in the case of voluntary muscle contractions, the sEMG signal is stochastic and denotes a sudden variation of both amplitude and frequency, deriving from the activation of the action potential. Therefore, we focused onto the TeagerKaiser Energy Operator (TKEO), which puts in evidence the instantaneous increase of the action potential and reduces the baseline noise [4]. A threshold algorithm was then implemented in TKEO's domain for detecting muscle activity, taking into consideration the minimum period of muscular activity, the minimum period of muscle inactivity and the margin of accuracy in the estimation of such measures [5,6].…”