Influence of spectral peaks on EMG parameter estimation for vibration-exercise analysis

Yaodan Xu, Xi Long, Zhe Luo (Corresponding author), Massimo Mischi, Lin Xu (Corresponding author)

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Abstract

Many studies have proposed vibration exercise (VE) as a novel training modality for neuromuscular conditioning and rehabilitation. Surface electromyography (sEMG) is widely used for effective measurement of muscle activity. Unfortunately, sharp spectral peaks (SSP) are usually present in the EMG signals recorded during VE. The explanation of these sharp peaks, as muscle activity or motion artifacts, is controversial, complicating EMG parameter extraction for the analysis of VE. The present study aims to quantify the impact of these SSP on the estimation of EMG parameters irrespective of their nature. High-density sEMG was therefore recorded from the biceps brachii muscle during VE with different vibration amplitudes (VA) and frequencies (VF). The power around (±0.5 Hz) VF and its first harmonic was calculated and normalized with the entire EMG power in order to obtain a relative power (PR) of these peaks. In addition, before and after excluding the SSP, three EMG parameters, i.e., mean frequency (MF), root mean square (RMS), and conduction velocity (CV), were estimated and compared. Our results reveal an average PR of 21.18±15.68 %. The relative difference in EMG RMS and MF are 12.2±3.8 % and 2.10±1.04 %, respectively. In addition, the impact of these peaks on the MF and RMS seems also to be affected by vibration conditions, such as VA and VF. However, the CV estimation seems not to be significantly influenced by these peaks, indicating these peaks to be primarily reflecting muscle activity and therefore should be included in VE EMG analysis.
Original languageEnglish
Article number9262944
Pages (from-to)14141-14147
Number of pages7
JournalIEEE Sensors Journal
Volume21
Issue number13
DOIs
Publication statusPublished - 1 Jul 2021

Keywords

  • EMG
  • vibration
  • exercise
  • parameter estimation
  • Force
  • Muscles
  • Harmonic analysis
  • Conduction velocity
  • Motion artifacts
  • Training
  • Vibration exercise
  • Vibrations
  • Electromyography
  • Sensors
  • Mean frequency
  • motion artifacts
  • vibration exercise
  • electromyography
  • mean frequency

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