@article {548411,
	title = {EMG analysis tuned for determining the timing and level of activation in different motor units},
	journal = {J Electromyogr KinesiolJ Electromyogr KinesiolJ Electromyogr Kinesiol},
	volume = {21},
	number = {4},
	year = {2011},
	note = {Lee, Sabrina S MMiara, Maria de BoefArnold, Allison SBiewener, Andrew AWakeling, James MengR01 AR055648/AR/NIAMS NIH HHS/R01 AR055648-03/AR/NIAMS NIH HHS/R01AR055648/AR/NIAMS NIH HHS/Research Support, N.I.H., ExtramuralEngland2011/05/17 06:00J Electromyogr Kinesiol. 2011 Aug;21(4):557-65. doi: 10.1016/j.jelekin.2011.04.003. Epub 2011 May 12.},
	month = {Aug},
	pages = {557-65},
	abstract = {Recruitment patterns and activation dynamics of different motor units greatly influence the temporal pattern and magnitude of muscle force development, yet these features are not often considered in muscle models. The purpose of this study was to characterize the recruitment and activation dynamics of slow and fast motor units from electromyographic (EMG) recordings and twitch force profiles recorded directly from animal muscles. EMG and force data from the gastrocnemius muscles of seven goats were recorded during in vivo tendon-tap reflex and in situ nerve stimulation experiments. These experiments elicited EMG signals with significant differences in frequency content (p<0.001). The frequency content was characterized using wavelet and principal components analysis, and optimized wavelets with centre frequencies, 149.94 Hz and 323.13 Hz, were obtained. The optimized wavelets were used to calculate the EMG intensities and, with the reconstructed twitch force profiles, to derive transfer functions for slow and fast motor units that estimate the activation state of the muscle from the EMG signal. The resulting activation-deactivation time constants gave r values of 0.98-0.99 between the activation state and the force profiles. This work establishes a framework for developing improved muscle models that consider the intrinsic properties of slow and fast fibres within a mixed muscle, and that can more accurately predict muscle force output from EMG.},
	keywords = {*Electromyography, *Recruitment, Neurophysiological, Animals, Goats, Motor Neurons/*physiology, Muscle, Skeletal/*physiology},
	isbn = {1873-5711 (Electronic)1050-6411 (Linking)},
	author = {Lee, S. S. and Miara Mde, B. and Arnold, A. S. and Biewener, A. A. and Wakeling, J. M.}
}
