Time-frequency analysis of surface myoelectric signals during athletic movement

Balestra, Gabriella; Frassinelli, Stefano; Knaflitz, Marco; Molinari, Filippo

doi:10.1109/51.982282

Cited by 30 publications

(19 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These changes are overlaid by length-independent modifications in the muscle, such as those due to fatigue, intramuscular temperature or pathological conditions, in addition to the latter physiological modifications, geometrical factors can play an important role in dynamic conditions, in most cases, especially with commonly used bipolar set-ups, it is not possible to recognise variations in the system geometry as important confounding factors in the interpretation of results. Spectral EMG analysis has been used in dynamic conditions to infer central control strategies (BALESTRA et al, 2001). The results shown in this study indicate that the interpretation of MNF changes during movement can be unreliable, especially if the electrode location with respect to the muscle fibres is not monitored.…”

Section: Discussionmentioning

confidence: 99%

Influence of muscle fibre shortening on estimates of conduction velocity and spectral frequencies from surface electromyographic signals

Schulte¹,

Farina

Merletti

et al. 2004

Med. Biol. Eng. Comput.

View full text Add to dashboard Cite

The study of surface electromyographic (EMG) signals under dynamic contractions is becoming increasingly important. However, knowledge of the methodological issues that may affect such analysis is still limited. The aim of the study was to analyse the effect of fibre shortening on estimates of conduction velocity (CV) and mean power spectral frequency (MNF) from surface EMG signals. Single fibre action potentials were simulated, as detected by commonly used spatial filters, for different fibre lengths. No physiological modifications were included with changes in fibre length, and thus only geometrical artifacts related to fibre shortening were investigated. The simulation results showed that the dependence of CV and MNF on fibre shortening is affected by the fibre location, electrode position and the spatial filter applied. With shortening of up to 50% for a fibre of 50 mm semi-length, the variations in CV and MNF estimates with shortening in bipolar recordings were 0.5% (CV) and 0.7% (MNF) for superficial fibres, and 3.6% and 5.1% for deeper fibres. Using the longitudinal double differential filter, under the same conditions, the percent variation was 0% and 0.2%, and 24.7% and 15.8%, respectively. The main conclusions were, first, muscle fibre shortening can significantly affect estimates of CV and MNF, especially for short fibre lengths. However, for long (semi-length >50 mm) and superficial fibres, this effect is limited for shortenings of up to 50% of the initial fibre length. Secondly, CV and MNF are almost equally affected by changes in muscle length; and, thirdly, sensitivity to fibre shortening depends on the spatial filter applied for signal detection.

show abstract

Section: Discussionmentioning

confidence: 99%

Influence of muscle fibre shortening on estimates of conduction velocity and spectral frequencies from surface electromyographic signals

Schulte¹,

Farina

Merletti

et al. 2004

Med. Biol. Eng. Comput.

View full text Add to dashboard Cite

show abstract

“…Nevertheless, the non-invasive nature of SEMG and its wide potential to offer global information about muscle activity have opened numerous application areas. The information extracted from SEMG signals is being exploited in several different clinical studies, mainly concerned with the timing of the muscle activation (MICERA et al, 2001), the EMG amplitude modulation (CLANCY and HOGAN, 1999), changes in the frequency content of the EMG signals (BALESTRA et al, 2001) and conduction velocity estimation (FARINA et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Correlation-based decomposition of surface electromyograms at low contraction forces

Holobar

Zazula

2004

Med. Biol. Eng. Comput.

130

105

View full text Add to dashboard Cite

The paper studies a surface electromyogram (SEMG) decomposition technique suitable for identification of complete motor unit (MU) firing patterns and their motor unit action potentials (MUAPs) during low-level isometric voluntary muscle contractions. The algorithm was based on a correlation matrix of measurements, assumed unsynchronised (uncorrelated) MU firings, exhibited a very low computational complexity and resolved the superimposition of MUAPs. A separation index was defined that identified the time instants of an MU's activation and was eventually used for reconstruction of a complete MU innervation pulse train. In contrast with other decomposition techniques, the proposed approach worked well also when the number of active MUs was slightly underestimated, if the MU firing patterns partly overlapped and if the measurements were noisy. The results on synthetic SEMG show 100% accuracy in the detection of innervation pulses down to a signal-to-noise ratio (SNR) of 10 dB, and 93+/-4.6% (mean+/-standard deviation) accuracy with 0 dB additive noise. In the case of real SEMG, recorded with an array of 61 electrodes from biceps brachii of five subjects at 10% maximum voluntary contraction, seven active MUs with a mean firing rate of 14.1 Hz were identified on average.

show abstract

“…Included among them are the Wigner-Ville distribution (WVD), the short-time Fourier transform (STFT) or spectrogram, the Choi-Williams distribution (CWD), the smoothed pseudo Wigner-Ville distribution (SPWVD), and wavelets. A comparison of the WVD, spectrogram, SPWVD and CWD is attained in [4], [3], [8] and [1]. The results show that the WVD is an efficient tool in the analysis of monocomponent signals; however, its performance in the analysis of multi-component signals is negatively affected by cross terms in the estimates of the spectral variables.…”

Section: Overviewmentioning

confidence: 99%

Wavelet-Based Signal Processing for Monitoring Discomfort and Fatigue

Potes¹,

Borries²,

Miosso³

2008

View full text Add to dashboard Cite

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MMM-YYYY) June 2008 REPORT TYPE Final Report DATES COVERED (From -To)September 2006 SPONSOR / MONITOR'S ACRONYMAFRL/RHPA SPONSOR/MONITOR'S REPORT NUMBER(S) AFRL-RH-WP-TR-2008-0066 DISTRIBUTION / AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. SUPPLEMENTARY NOTES88ABW/PA cleared 4Aug08, WPAFB-08-4987. ABSTRACTMuscle fatigue involves both a decrease in the frequency and increase in the amplitude of a surface electromyographic (SEMG) signal. Muscle fatigue is also related to a decrease of the force impeding to reach the same initial level of the maximum voluntary contraction (MVC). To determine indices of muscle fatigue, a method is proposed to estimate both the instantaneous frequency (IF) and the instantaneous amplitude (IA) by decomposing the SEMG signal using a filter bank. A linear regression model was adopted to compute the IF and IA slopes. These slopes were then classified in muscle increase force, recovery, muscle decrease force and fatigue by using a joint analysis of frequency and amplitude. SEMG signals were recorded from 26 normal human subjects when doing an exertion of 70% and 100% of their MVC during a session of eight hours. It was found that slopes derived from the proposed filter bank are equivalent to those slopes derived from the spectrogram and the smoothed pseudo Wigner-Ville distribution. Furthermore, slopes derived from the filter bank indicated that they can be used as indices to determine muscle fatigue. These results were confirmed by correlating indices of muscle fatigue with perceived levels of discomfort reported by the subjects after performing an exertion of 70% MVC in hours two, four, and six.

show abstract

Time-frequency analysis of surface myoelectric signals during athletic movement

Cited by 30 publications

References 8 publications

Influence of muscle fibre shortening on estimates of conduction velocity and spectral frequencies from surface electromyographic signals

Influence of muscle fibre shortening on estimates of conduction velocity and spectral frequencies from surface electromyographic signals

Correlation-based decomposition of surface electromyograms at low contraction forces

Wavelet-Based Signal Processing for Monitoring Discomfort and Fatigue

Contact Info

Product

Resources

About