THE EMG-FORCE RELATIONSHIP OF THE CAT SOLEUS MUSCLE AND ITS ASSOCIATION WITH CONTRACTILE CONDITIONS DURING LOCOMOTION

Citation
Ac. Guimaraes et al., THE EMG-FORCE RELATIONSHIP OF THE CAT SOLEUS MUSCLE AND ITS ASSOCIATION WITH CONTRACTILE CONDITIONS DURING LOCOMOTION, Journal of Experimental Biology, 198(4), 1995, pp. 975-987
Citations number
47
Categorie Soggetti
Biology
ISSN journal
00220949
Volume
198
Issue
4
Year of publication
1995
Pages
975 - 987
Database
ISI
SICI code
0022-0949(1995)198:4<975:TEROTC>2.0.ZU;2-H
Abstract
The relationship between force and electromyographic (EMG) signals of the cat soleus muscle was obtained for three animals during locomotion at five different speeds (154 steps), using implanted EMG electrodes and a force transducer. Experimentally obtained force-IEMG (=integrate d EMG) relationships were compared with theoretically predicted instan taneous activation levels calculated by dividing the measured force by the predicted maximal force that the muscle could possibly generate a s a function of its instantaneous contractile conditions, In addition, muscular forces were estimated from the corresponding EMG records exc lusively using an adaptive filtering approach. Mean force-IEMG relatio nships were highly non-linear but similar in shape for different cats and different speeds of locomotion. The theoretically predicted activa tion-time plots typically showed two peaks, as did the IEMG-time plots . The first IEMG peak tended to be higher than the second one and it a ppeared to be associated with the initial priming of the muscle for fo rce production at paw contact and the peak force observed early during the stance phase. The second IEMG peak appeared to be a burst of high muscle activation, which might have compensated for the levels of mus cle length and shortening velocity that were suboptimal during the lat ter part of the stance phase. Although it was difficult to explain the soleus forces on the basis of the theoretically predicted instantaneo us activation levels, it was straightforward to approximate these forc es accurately from EMG data using an adaptive filtering approach.