Biology subjects

Millard, M.

Publications and source records attributed to Millard, M..

4 recordsLinked to original sources

bioRxiv2025

The size of the functional base of support decreases with age

Falls occur more often as we age. To identify people at risk of falling, balance analysis requires an accurate base-of-support model. We previously developed a functional base-of-support (fBOS) model for standing young adults and showed that its area is smaller than the footprint area. Our fBOS model is a polygon that contains centre-of-pressure (COP) trajectories recorded as standing participants move their COP in the largest possible loop while keeping their feet flat on the ground. Here we assess how the size of the fBOS changes with age by comparing 38 younger (YA), 14 middle-aged (MA), and 34 older adults (OA). The fBOS area is smaller in older adults: OA area is 58% of the YA area (p < 0.001), and 59% of the MA area (p = 0.001), with no difference between YA and MA. The reduction in fBOS area among the OA is primarily caused by a reduction in the length of the fBOS. In addition, among older adults smaller fBOS areas correlated with a lower score on the Short Physical Performance Battery ({tau}=0.28, p = 0.04), a reduced walking speed ({tau}=0.25, p = 0.04), and a higher frailty level (p = 0.09). So that others can extend our work, we have made our fBOS models available online.

bioengineering↗

bioRxiv2025

A polygon model of the functional base-of-support improves the accuracy of balance analysis

Mathematical balance models have the potential to identify people at risk of falling. However, most balance models depend on a model of the base-of-support (BOS) of the feet to calculate how well someone is balancing. Here, we evaluate the functional base-of-support (fBOS) during standing: the convex polygon on the bottom of the foot that can support a large fraction of the bodys weight. First, we develop a geometric model of the fBOS by measuring the center-of-pressure (COP) and kinematic data of the feet of 27 younger adults instructed to move their body in large loops without taking a step. Next, we extract a planar convex polygon that contains the COP data. Finally, we compare the area of this fBOS model to a marker-based BOS model before evaluating if the fBOS differs across four common conditions: footwear, stance-width, foot dominance, and during single and double-stance. We found that the fBOS is much smaller (23% the size) than a markerbased BOS model. Our analysis suggests that using the fBOS, rather than a marker-based BOS, can improve the accuracy of the margin-of-stability by 20% of foot width and 16% of the length. In addition, we found that the fBOS area does not differ across footwear (p = 0.88), stancewidth (p = 0.88), and foot dominance (p = 0.68), but during single stance, the fBOS is 17% (p = 0.0003) larger than during double-stance. The variability of the fBOS area suggests that future studies should establish the repeatability and reliability of the assessment and systematically study the effects of different types of footwear. We have put the fBOS models, example data, and code in the public domain to help others build on our work.

bioengineering↗

bioRxiv2024

A benchmark of muscle models to length changes great and small

Digital human body models are used to simulate injuries that occur as a result of vehicle collisions, vibration, sports, and falls. Given enough time the bodys musculature can generate force, affect the bodys movements, and change the risk of some injuries. The finite-element code LS-DYNA is often used to simulate the movements and injuries sustained by the digital human body models as a result of an accident. In this work, we evaluate the accuracy of the three muscle models in LS-DYNA (MAT_156, EHTMM, and the VEXAT) when simulating a range of experiments performed on isolated muscle: force-length-velocity experiments on maximally and sub-maximally stimulated muscle, active-lengthening experiments, and vibration experiments. The force-length-velocity experiments are included because these conditions are typical of the muscle activity that precedes an accident, while the active-lengthening and vibration experiments mimic conditions that can cause injury. The three models perform similarly during the maximally and sub-maximally activated force-length-velocity experiments, but noticeably differ in response to the active-lengthening and vibration experiments. The VEXAT model is able to generate the enhanced forces of biological muscle during active lengthening, while both the MAT_156 and EHTMM produce too little force. In response to vibration, the stiffness and damping of the VEXAT model closely follows the experimental data while the MAT_156 and EHTMM models differ substantially. The accuracy of the VEXAT model comes from two additional mechanical structures that are missing in the MAT_156 and EHTMM models: viscoelastic cross-bridges, and an active titin filament. To help others build on our work we have made our benchmark simulations and model code publicly available.

bioengineering↗

bioRxiv2023

A three filament mechanistic model of musculotendon force and impedance

The force developed by actively lengthened muscle depends on different structures across different scales of lengthening. For small perturbations, the active response of muscle is well captured by a linear-time-invariant (LTI) system: a stiff spring in parallel with a light damper. The force response of muscle to longer stretches is better represented by a compliant spring that can fix its end when activated. Experimental work has shown that the stiffness and damping (impedance) of muscle in response to small perturbations is of fundamental importance to motor learning and mechanical stability, while the huge forces developed during long active stretches are critical for simulating and predicting injury. Outside of motor learning and injury, muscle is actively lengthened as a part of nearly all terrestrial locomotion. Despite the functional importance of impedance and active lengthening, no single muscle model has all of these mechanical properties. In this work, we present the viscoelastic-crossbridge active-titin (VEXAT) model that can replicate the response of muscle to length changes great and small. To evaluate the VEXAT model, we compare its response to biological muscle by simulating experiments that measure the impedance of muscle, and the forces developed during long active stretches. In addition, we have also compared the responses of the VEXAT model to a popular Hill-type muscle model. The VEXAT model more accurately captures the impedance of biological muscle and its responses to long active stretches than a Hill-type model and can still reproduce the force-velocity and force-length relations of muscle. While the comparison between the VEXAT model and biological muscle is favorable, there are some phenomena that can be improved: the low frequency phase response of the model, and a mechanism to support passive force enhancement.

bioengineering↗