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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bonato, P. Roy, S.H. Knaflitz, M. De Luca, C.J. |
| Copyright Year | 1997 |
| Description | Author affiliation: Dept. of Electron., Politecnico di Torino, Italy (Bonato, P.) |
| Abstract | Over the past decade the analysis of the surface electromyographic (SEMG) signal has been shown effective in objectively quantifying paraspinal muscle impairments as a means of optimizing the delivery of low back pain (LBP) rehabilitation procedures. This approach to back impairment classification is based on indices of muscle fatigue derived from spectral estimates of the SEMG signal. A primary limitation to this technique is that the quantification procedure for spectral analysis assumes signal stationarity. This pre-condition can only be satisfied by recording the SEMG signal during isometric, constant-force contractions. This is a serious limitation to the technique since many dynamic activities are commonly associated with LBP injury. Fortunately, however, recent developments in the field of signal processing have produced methods of time-frequency (TF) analysis that are able to extract spectral information from nonstationary signals. Preliminary work has demonstrated that specific transforms belonging to the Cohen Class appear robust enough to derive spectral indices of fatigue from nonstationary SEMG signals recorded during cyclical dynamic contraction conditions. In this paper we present a procedure to measure localized muscle fatigue during dynamic contractions and we compare the results obtained during static vs. dynamic contractions of paraspinal muscles. |
| Starting Page | 1263 |
| Ending Page | 1266 |
| File Size | 435128 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780342623 |
| ISSN | 1094687X |
| DOI | 10.1109/IEMBS.1997.756604 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1997-10-30 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Muscles Fatigue Signal analysis Signal processing Pain Life estimation Spectral analysis Injuries Time frequency analysis Information analysis |
| Content Type | Text |
| Resource Type | Article |
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