Biology subjects

Kudzia, P.

Publications and source records attributed to Kudzia, P..

3 recordsLinked to original sources

bioRxiv2023

Neuromuscular fatigue reduces responsiveness when controlling leg external forces

In legged movement, our legs push against the ground, generating external force vectors that enable agile movements. Neuromuscular fatigue can reduce agility by causing physiological changes, such as slowing muscle reaction time, altering proprioception, and delaying neuromuscular control. Fatigue may deteriorate the nervous systems control of leg external forces, contributing to reductions in agility. In this study, we investigated the effect of fatigue on the performance of the nervous system in controlling the vertical component of leg external force ground reaction forces. We hypothesized that increased leg fatigue would lead to declines in both the responsiveness (speed) and accuracy of leg force control. To test this hypothesis, we used an apparatus that allowed participants to exert controlled vertical forces with one leg against a force plate while immobilizing the rest of their bodies. Participants adjusted their leg external force to match step targets displayed on a screen. We induced fatigue by having participants maintain submaximal leg forces, and we measured leg force control performance between fatigue trials. Results showed a significant 26% reduction in mean maximum force production, leading to a substantial decline in leg force control responsiveness, as evidenced by a 23% increase in rise time and a 25% narrowing of bandwidth. However, fatigue did not significantly reduce leg force control accuracy. Understanding the effects of fatigue on leg force control can inform the development of strategies and technologies to sustain agile performance, even in the presence of fatigue. New and NoteworthyWe developed a method to probe the influence of neuromuscular fatigue on the control of leg external forces. Our findings demonstrate that while fatigue significantly diminishes responsiveness (speed), it does not compromise the accuracy of control. These insights enhance our understanding of legged agility and could guide the development of strategies for optimizing leg force control performance. This study paves the way for future research aimed at identifying and employing effective strategies to maintain agility in the face of fatigue.

neuroscience↗

bioRxiv2021

Characterizing the performance of human leg external force control

Our legs act as our primary contact with the surrounding environment, generating external forces that enable agile motion. To be agile, the nervous system has to control both the magnitude of the force that the feet apply to the ground and the point of application of this force. The purpose of this study was to characterize the performance of the healthy human neuromechanical system in controlling the force-magnitude and position of an externally applied force. To accomplish this, we built an apparatus that immobilized participants but allowed them to exert variable but controlled external forces with a single leg onto a ground embedded force plate. We provided real-time visual feedback of either the leg force-magnitude or position that participants were exerting against the force platform and instructed participants to best match their real-time signal to prescribed target step functions. We tested target step functions of a range of sizes and quantified the responsiveness and accuracy of the control. For the control of force-magnitude and for intermediate step sizes of 0.45 bodyweights, we found a bandwidth of 1.8{+/-}0.5 Hz, a steady-state error of 2.6{+/-}0.9%, and a steady-state variability of 2.7{+/-}0.9%. We found similar control performance in terms of responsiveness and accuracy across step sizes and between force-magnitude and position control. Increases in responsiveness correlated with reductions in other measures of control performance, such as a greater magnitude of overshooting. We modelled the observed control performance and found that a second-order model was a good predictor of external leg force control. We discuss how benchmarking force control performance in young healthy humans aids in understanding differences in agility between humans, between humans and other animals, and between humans and engineered systems.

physiology↗

bioRxiv2021

Estimating body segment parameters from three-dimensional human body scans

Body segment parameters are inputs for a range of applications. The estimation of body segment parameters that are participant-specific is desirable as it requires fewer prior assumptions and can reduce outcome measurement errors. Commonly used methods for estimating participant-specific body segment parameters are either expensive and out of reach (medical imaging), have many underlying assumptions (geometrical modelling) or are based on a specific subset of a population (regression models). Our objective was to develop a participant-specific 3D scanning and body segmentation method that estimates body segment parameters without any assumptions about the geometry of the body, ethnic background, and gender, is low-cost, fast, and can be readily available. Using a Microsoft Kinect camera, we developed a 3D surface scanning protocol that estimated participant-specific body segment parameters. To evaluate our system, we performed repeated 3D scans of 21 healthy participants (10 male, 11 female). We used open-source software to segment each body scan into 16 segments (head, torso, abdomen, pelvis, left and right hand, forearm, upper arm, foot, shank and thigh) and wrote custom software to estimate each segments mass, mass moment of inertia in the three principal orthogonal axes relevant to the center of the segment, longitudinal length, and center of mass. We compared our body segment parameter estimates to those obtained using two comparison methods and found that our system was consistent in estimating total body volume between repeated scans (male p=0.1194, female p = 0.2240), estimated total body mass without significant differences when compared to our comparison method and a medical scale (male p=0.8529, female p = 0.6339), and generated consistent and comparable estimates across all of the body segment parameters of interest. The work here outlines an inexpensive 3D surface scanning approach for estimating a range of participant-specific body segment parameters.

bioengineering↗