Elsevier

Journal of Biomechanics

Volume 89, 24 May 2019, Pages 134-138
Journal of Biomechanics

Short communication
Muscle-specific indices to characterise the functional behaviour of human lower-limb muscles during locomotion

https://doi.org/10.1016/j.jbiomech.2019.04.027Get rights and content

Abstract

The mechanical output of a muscle may be characterised by having distinct functional behaviours, which can shift to satisfy the varying demands of movement, and may vary relative to a proximo-distal gradient in the muscle-tendon architecture (MTU) among lower-limb muscles in humans and other terrestrial vertebrates. We adapted a previous joint-level approach to develop a muscle-specific index-based approach to characterise the functional behaviours of human lower-limb muscles during movement tasks. Using muscle mechanical power and work outputs derived from experimental data and computational simulations of human walking and running, our index-based approach differentiated known distinct functional behaviours with varying mechanical demands, such as greater spring-like function during running compared with walking; with anatomical location, such as greater motor-like function in proximal compared with the distal lower-limb muscles; and with MTU architecture, such as greater strut-like muscles fibre function compared with the MTU in the ankle plantarflexors. The functional indices developed in this study provide distinct quantitative measures of muscle function in the human lower-limb muscles during dynamic movement tasks, which may be beneficial towards tuning the design and control strategies of physiologically-inspired robotic and assistive devices.

Introduction

Muscles generate force and do work to produce body movement. Muscles often contract to generate positive mechanical work and power. However, muscles may also contract isometrically or absorb energy, performing a range of functions across movement tasks and species (Dickinson et al., 2000). These functions can be characterised into four distinct behaviours: (1) a motor that generates positive work; (2) a spring that stores and recovers elastic strain energy; (3) a strut that generates significant force with minimal length change; and (4) a damper that lengthens to absorb energy. These functions depend on a range of factors, from interactions between the external environment and the body to the intrinsic properties of the muscle (Biewener, 2016). One clear example is the change in human ankle plantarflexor work that occurs with a shift in whole body mechanical demands during sprinting (Lai et al., 2016), or in turkey leg extensors during incline running (Roberts et al., 1997, Roberts and Scales, 2004). Other studies have shown that muscle function varies with a proximo-distal gradient of lower-limb muscle organisation (Biewener, 2016), where more distal muscles have been shown to exhibit strut-like, quasi-isometric muscle fibre behaviour favouring force development and spring-like storage of elastic strain energy in humans (e.g. Lai et al., 2015), wallabies (Biewener et al., 1998), and turkeys Roberts et al., 1997). In contrast, more proximal muscles generally favour work modulation (Biewener, 1998, Biewener and Daley, 2007).

Despite our understanding of how muscle function can vary with mechanical demand and anatomical location, there is yet to be a quantitative approach capable of comparing the function of different muscles and how function varies across locomotor demands. Addressing these limitations can assist in tuning the design and control strategies of physiological-inspired robotics and assistive devices that can mimic the diversity of human movement (Grimmer and Seyfarth, 2014). A promising index-based approach was introduced by Qiao and Jindrich (2016) that characterised joint function during locomotion. Our study adapts this approach to define muscle-specific parameters and, based on experimental data and computational simulations, applies the approach to characterise the functional behaviours of the human lower-limb muscles during locomotion. Using simulations of walking and running, we evaluated the approach by differentiating existing understanding of muscle function, including (1) greater spring-like function during running compared with walking, (2) greater motor-like function in the proximal limb muscles, and (3) greater strut-like function in distal muscle fibres compared with the MTU.

Section snippets

Experimental protocol

Experimental data were taken from ten participants (9 males, 1 female; 27 ± 5.6 y.o.; 1.81 ± 0.07 m; 80.2 ± 11.7 kg) who were part of a larger study (Lai et al., 2015). Each participant gave their informed consent and the relevant ethics committees approved the study (University of Queensland ethics #: 2012001215). Marker trajectories and ground reaction forces were extracted during walking and running at steady-state speeds of 1.4 m s−1 and 4 m s−1, respectively.

3D trajectories of 36

Results

The selected muscles generated the majority of total negative (68%) and positive (69%) MTU power and work done by the lower-limb during walking and running (Fig. 1, Fig. 2). Specifically, GMAX, VL, MG, and SO generated substantial MTU and muscle fibre power during the stance phase of walking and running; whereas, bi-articular RF and BF generated substantial negative and positive power during the swing phase of walking and running. The functional indices of the selected muscles varied with

Discussion

A muscle’s function is commonly characterised by its mechanical force and work output. We show that the functional index approach introduced here is capable of quantitatively characterising and differentiating functional variations of several human lower-limb muscles across gait-related whole-body mechanical demands, their anatomical location within the limb, and between the MTU and its muscle fibres. For example, our muscle-specific index approach demonstrated the shift to more spring-like

Conflict of interest statement

There are no conflicts of interest.

Acknowledgements

We thank Glen Lichtwark for assistance during data collection. We gratefully acknowledge funding from NIH Grant 2R01AR055648.

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