Rapid muscle volume prediction using anthropometric measurements and population-derived statistical models.

Knowledge of subject-specific muscle volumes may be used as surrogates for evaluating muscle strength and power generated by 'fat-free' muscle mass. This study presents population-based statistical learning approaches for predicting 'fat-free' muscle volume from known anthropometric measurements. Using computed tomography (CT) imaging data to obtain lower-limb muscle volumes from 50 men and women, this study evaluated six statistical learning methods for predicting muscle volumes from anthropometric measurements: (i) stepwise regression, (ii) linear support vector machine (SVM), (iii) 2nd-order polynomial SVM, (iv) linear partial least squares regression (PLSR), (v) quadratic PLSR, and (vi) 3rd-order spline fit PLSR. These techniques have successfully been demonstrated in bioengineering applications and freely available in open-source toolkits. Analysis revealed that separating a general population into sexes and/or cohorts based on adipose level may improve prediction accuracies. The most important measures that statistically influence muscle volume predictions were shank girth, followed by sex and finally leg length, as identified using stepwise regression. SVM learning predicted muscle volume with an accuracy of 85 ± 4% when using linear interpolation, but performed poorly with an accuracy of 59 ± 6% using polynomial interpolation. The simpler linear PLSR exhibited muscle volume prediction accuracy of 87 ± 2%, while quadratic PLSR was slightly reduced at 82 ± 3%. For the spline fit PLSR, high accuracy was observed on the training data set (~ 99%) but over-fitting (a drawback of high-interpolation methods) resulted in erroneous predictions on testing data, and hence, the model was deemed unsuitable. In conclusion, use of linear PLSR models with variables of sex, leg length, and shank girth is a useful tool for predicting 'fat-free' muscle volume.

Can research align with service? Lessons learned from the Big Experiment and National Biomechanics Day.

Public engagement is an important role for the university academic, but is often neglected due to perceived lack of time and prioritized commitments in research and teaching. Yet, public engagement events offer an untapped opportunity for researchers to collect data from members of the general public who arrive on site at university labs. These engagement events could allow for data collection as part of didactic and demonstrative outreach events to be used in research and science. In this proof of concept study, a collaborative group of international researchers investigated the feasibility of embedding research quality assessment into events surrounding National Biomechanics Day. The Big Experiment collected data on 501 secondary school students (age range: 13 to 18 years) across 9 university sites within a 24-hour period. Data included maximal vertical jump height and self-reported physical activity levels. Vertical jump height was positively correlated to participant height, but not age or body mass. Very physically active students had significantly higher vertical jump heights than individuals who reported being somewhat or not physically active. This feasibility project demonstrates that with substantial preparation and a simple research design, focused research questions can be incorporated into educational outreach initiatives and ultimately provide a rich data source.

Adding muscle where you need it: non-uniform hypertrophy patterns in elite sprinters.

Sprint runners achieve much higher gait velocities and accelerations than average humans, due in part to large forces generated by their lower limb muscles. Various factors have been explored in the past to understand sprint biomechanics, but the distribution of muscle volumes in the lower limb has not been investigated in elite sprinters. In this study, we used non-Cartesian MRI to determine muscle sizes in vivo in a group of 15 NCAA Division I sprinters. Normalizing muscle sizes by body size, we compared sprinter muscles to non-sprinter muscles, calculated Z-scores to determine non-uniformly large muscles in sprinters, assessed bilateral symmetry, and assessed gender differences in sprinters' muscles. While limb musculature per height-mass was 22% greater in sprinters than in non-sprinters, individual muscles were not all uniformly larger. Hip- and knee-crossing muscles were significantly larger among sprinters (mean difference: 30%, range: 19-54%) but only one ankle-crossing muscle was significantly larger (tibialis posterior, 28%). Population-wide asymmetry was not significant in the sprint population but individual muscle asymmetries exceeded 15%. Gender differences in normalized muscle sizes were not significant. The results of this study suggest that non-uniform hypertrophy patterns, particularly large hip and knee flexors and extensors, are advantageous for fast sprinting.

Toward modeling locomotion using electromyography-informed 3D models: application to cerebral palsy.

This position paper proposes a modeling pipeline to develop clinically relevant neuromusculoskeletal models to understand and treat complex neurological disorders. Although applicable to a variety of neurological conditions, we provide direct pipeline applicative examples in the context of cerebral palsy (CP). This paper highlights technologies in: (1) patient-specific segmental rigid body models developed from magnetic resonance imaging for use in inverse kinematics and inverse dynamics pipelines; (2) efficient population-based approaches to derive skeletal models and muscle origins/insertions that are useful for population statistics and consistent creation of continuum models; (3) continuum muscle descriptions to account for complex muscle architecture including spatially varying material properties with muscle wrapping; (4) muscle and tendon properties specific to CP; and (5) neural-based electromyography-informed methods for muscle force prediction. This represents a novel modeling pipeline that couples for the first time electromyography extracted features of disrupted neuromuscular behavior with advanced numerical methods for modeling CP-specific musculoskeletal morphology and function. The translation of such pipeline to the clinical level will provide a new class of biomarkers that objectively describe the neuromusculoskeletal determinants of pathological locomotion and complement current clinical assessment techniques, which often rely on subjective judgment. WIREs Syst Biol Med 2017, 9:e1368. doi: 10.1002/wsbm.1368 For further resources related to this article, please visit the WIREs website.