Meeting Abstract
66.3 Thursday, Jan. 6 Neuromuscular Activation Dynamics from Different Types of Motor Units for Predicting Muscle Forces During Locomotion LEE, S/SM*; BIEWENER, A/A; DEBOEF MIARA, M; WAKELING, J/M; Simon Fraser University, Burnaby, Canada; Harvard University, Cambridge, MA; Harvard University, Cambridge, MA; Simon Fraser University, Burnaby, Canada sabrina_lee_4@sfu.ca
Neuromuscular activation dynamics are critical to muscle force development. Activation dynamics vary with fiber-type, yet these features have not been considered in musculoskeletal models often used to interpret human motor function. To address this, we collected electromyography (EMG) and tendon force data in goat distal leg muscles that correspond to slow and fast motor unit recruitment through a series of in vivo tendon-tap reflexes and in situ nerve stimulation experiments to test whether activation dynamics can be predicted to better understand force development during locomotor behaviour. We developed a set of optimized wavelets that characterize the frequency content of the EMG signals from the slow and fast motor units using wavelet and principal component analysis. Force development and relaxation rates for slow and fast motor units during tendon-tap reflexes and in situ twitches were also characterized using similar methods. Transfer functions were derived from these data to estimate the activation state of the muscle from the EMG intensity using first order dynamics and a bilinear differential equation. Activation and deactivation time constants estimates resulted in R2 values of approximately 0.98 between the activation state of the muscle and the force-time curves. Validation of the transfer functions were conducted by calculating the activation states for in situ stimulation experiments of unfused tetani at different stimulation frequencies and comparing those to the force-time curves. These results provide a novel method of EMG processing, which will form a fundamental basis for developing muscle models employed in forward dynamics modeling of motor function in humans and other animals. (supported by NIH R01AR055648)
