Meeting Abstract

43.4  Saturday, Jan. 5  A two-element Hill-type model to predict muscle forces LEE, SSM*; BIEWENER, AA; DE BOEF MIARA, M; ARNOLD, AS; WAKELING, JM; Simon Fraser University; Harvard University; Harvard University; Harvard University; Simon Fraser University lee.sabrina.sm@gmail.com

Muscle models are commonly used to quantify and interpret musculoskeletal function. However, most previous models have assumed homogenous fibre type distribution within the muscle with only a single contractile element, and validation of these models has been limited to in situ experiments that do not represent the muscle’s dynamic behavior in vivo. The purposes of this study were to develop and test a two-element Hill-type muscle model with independently activated slow and fast fibre contractile elements. The model was evaluated under in situ and in vivo conditions by comparing the predicted forces to directly measured forces. We recorded electromyography (EMG), tendon force, and sonomicrometry (fascicle length) data in the lateral and medial gastrocnemii of six goats during 1) in situ nerve stimulation experiments (active and passive force length curves and tetanic contractions with different stimulation patterns) and 2) in vivo experiments (goats walked, trotted, and galloped on a treadmill). Activation states of different motor units were quantified via wavelet analysis of the EMG data and tuned transfer functions. By comparing the coefficients of determination between the predicted and measured forces, we found that the two-element model predicted muscle forces with up to 7.6% and 8.2% greater accuracy than several commonly used, one-element models for the in situ and in vivo conditions, respectively. Root mean square errors were up to 21% lower for the two-element model than for the one-element models tested. This study offers a novel Hill-type muscle model, validated against in vivo forces, that can independently activate slow and fast contractile elements. This model has the potential to improve studies of locomotor tasks where recruitment patterns of different motor unit types differ.(NIH R01AR055648)