TRUNK MOTION DURING A HALF-MARATHON: THE IMPACT OF PERCEIVED FATIGUE ON MOTION STABILITY AND SMOOTHNESS

Our objective was to investigate the effects of acute fatigue on stability and smoothness of trunk motion during a half marathon. 13 recreational runners were fitted with a GNSS-IMU sensor on their chest. Every 10 minutes of the race, the participant pronounced their perceived fatigue, recorded by a smartphone attached to the arm. We divided the race into 8 equal segments, corresponding to one fatigue score per segment, and considered only level running. Based on mediolateral acceleration and running velocity (v), stability was characterized by spectral entropy, RMS of acceleration (RMSA), and autocorrelation between successive steps and strides; smoothness by jerk cost (JC), spectral arc length (SPARC), and inverse number of peaks (IPV) of v. Both RMSA and JC increased significantly shortly after race onset. RMSA increased significantly at a lower perceived fatigue level, while JC increased at a higher level. Whereas other measures did not change substantially, RMSA and JC showed a clear change with acute fatigue and also differentiated well between the five fastest and five slowest runners. With increasing perceived fatigue, both parameters showed a higher change for ‘slow’ group. This study highlights the loss of stability and smoothness in running due to acute fatigue and the importance of simultaneously measuring perceived fatigue and trunk biomechanics under real-world conditions

CONCURRENT ASSESSMENT OF SYMMETRY, VARIABILITY, AND COMPLEXITY OF STRIDE DURING PROLONGED OUTDOOR RUNNING

The aim of this study was to analyse the influence of acute fatigue on the asymmetry, variability, and complexity of the running pattern. We equipped 11 half-marathon participants with an inertial measurement unit (IMU) on each foot and a global navigation satellite system (GNSS)-IMU sensor on chest. Every 10 minutes of the race, the participant pronounced their perceived rating-of-fatigue (ROF) on a scale of 1 to 10. We divided the race into 8 equal segments, with one ROF score per segment, and included only the flat running parts. Temporal gait parameters were extracted using validated algorithms, followed by the computation of their asymmetry, and the variability and complexity of the cycle time (CT). Gait asymmetry increased significantly toward the end of the race and at higher perceived fatigue; faster runners showed a greater increase in asymmetry. CT variability increased significantly at the beginning of the race and then remained stable for all participants, but faster runners showed up to 20% less variability. No significant change was observed in CT complexity. This study highlights the increase in asymmetry and variability due to acute fatigue, with differences between fast/slow runners, and the importance of simultaneously measuring perceived fatigue and gait parameters under real-world conditions

Towards real-world biomechanical analysis of performance and functional capacity using wearable sensors

Today, wearable heart rate monitors are an integral part of runners' training sessions, with heart rate data routinely used to assess effort intensity and stress on the body. As athletes translate their physical capacity into performance on the field through their movement, biomechanical assessment can provide valuable information that complements physiological assessment. However, the potential of using biomechanical information in the evaluation of training sessions and standardized tests in practice remains largely untapped, partly because the assessment devices remain cumbersome to use and often require long post-processing, as well as programming skills. The proposed work aims to realize this potential by developing field methods for performance and capacity evaluation using portable inertial measurement units (IMUs) and Global Navigation Satellite System (GNSS) receivers. Running performance can be characterized by the ability to maintain appropriate running technique despite fatigue, while keeping the effort intensity prescribed by the coach, or planned as a pacing strategy. In this work, a systematic review was conducted to examine and synthesize the results of fatigue protocols in running, fol-lowed by continuous measurements during a competition to confirm the trends obtained from the review with data from the field and to measure the changes in running technique due to fatigue. In addition, models were developed to accurately estimate running power using foot-worn IMU over a range of speeds and inclines and validated using gold standard methods in the laboratory, to better characterize running intensity. The second part of this work consisted of investigating the ability of IMUs and GNSS to improve the evaluation of athletes in standardized tests, referred to as their functional capacity. Functional capacity is typically used by coaches to develop appropriate training loads for athletes. This work presented validated methods to instrument common functional tests with wearable sensors to measure the speed, agility, and endurance of athletes in the field. In addition, these methods enable the extraction and a deeper analysis of relevant biomechanical parameters that contribute to the measured capacity and help the sporting staff understand athletes' strengths and weaknesses in detail. All of the research conducted and methods developed in this work are based on various combinations of a minimal body-worn sensor setup with foot-worn IMUs and a single trunk-worn IMU-GNSS unit. The signal processing algorithms and models developed in this work allow the recorded signals to be translated into easily interpretable and actionable information. Based on this information, coaches and physical therapists can develop customized training programs that target the relevant parameters. The proposed sensor setups and methods have been used and validated in a variety of situations, such as pre-season testing of a professional soccer team, training sessions of elite sprinters, the Lausanne half-marathon race, etc., highlighting their potential for real-world application. I believe that this work will help pave the way towards a deeper understanding of the biomechanical contributions to performance in running and provide new tools for the development of personalized training and rehabilitation programs, with the goal of optimizing positive adaptation to training stimuli, thereby improving performance and reducing the incidence of injury

Benchmarking gait models for simulating the influence of body weight unloading on human gait

Mechanical Engineerin

Simulation of human gait with body weight support: Benchmarking models and unloading strategies

This dataset contains the gait parameter data generated from the simulation of human gait models with body weight support systems. The included gait models are: Simplest walking model, SLIP model and Muscle reflex model. The simulation body weight support strategies are: constant force, counterweight and tuned spring. The code used for simulation is uploaded in a separate dataset. Since the gait data extraction and processing from the simulation is not fully automated, the current dataset is uploaded separately from the simulation code. The 'boundedline' package used for plotting is credited to Kelly Kearney and obtained from https://github.com/kakearney/boundedline-pkg The 'superbar' package used for plotting is credited to Scott Lowe and obtained from https://in.mathworks.com/matlabcentral/fileexchange/57499-superbar These packages are included in the dataset upload as they are necessary for making the plots

Simulation of human gait with body weight support: benchmarking models and unloading strategies

This code package was generated for two purposes: First, to evaluate the accuracy of some of the most prevalent walking models in replicating human walking under the influence of Constant-Force BWS: The Simplest Walking model (SW), the Spring-Loaded Inverted Pendulum model (SLIP) and the Muscle-Reflex (MR) gait model using human experimental data. This experimental data is obtained from a systematic review, the dataset for which can be found at: https://doi.org/10.4121/uuid:6c42843c-6b07-4255-ad0f-7f6d8a994251. Second, three realizations of body weight support strategies, based on Constant-Force (CF), Counterweight (CW) and Tuned-Spring (TS) approaches, are compared to each other in terms of their influence on gait parameters. The model files and scripts necessary for running the simulations are included here. Since the gait data extraction and processing from the simulation is not fully automated, the gait parameter data used for the manuscript is also incorporated here. A 'readme' text file is included for more details

Simulation of human gait with body weight support: benchmarking models and unloading strategies

Background Gait training with partial body weight support (BWS) has become an established rehabilitation technique. Besides passive unloading mechanisms such as springs or counterweights, also active systems that allow rendering constant or modulated vertical forces have been proposed. However, only pilot studies have been conducted to compare different unloading or modulation strategies, and conducting experimental studies is costly and time-consuming. Simulation models that predict the influence of unloading force on human walking may help select the most promising candidates for further evaluation. However, the reliability of simulation results depends on the chosen gait model. The purpose of this paper is two-fold: First, using human experimental data, we evaluate the accuracy of some of the most prevalent walking models in replicating human walking under the influence of Constant-Force BWS: The Simplest Walking model (SW), the Spring-Loaded Inverted Pendulum model (SLIP) and the Muscle-Reflex (MR) gait model. Second, three realizations of BWS, based on Constant-Force (CF), Counterweight (CW) and Tuned-Spring (TS) approaches, are compared to each other in terms of their influence on gait parameters. Methods We conducted simulations in Matlab/Simulink to model the behaviour of each gait model under all three BWS conditions. Nine simulations were undertaken in total and gait parameter response was analysed in each case. Root mean square error (mrmse) w.r.t human data was used to compare the accuracy of gait models. The metrics of interest were spatiotemporal parameters and the vertical ground reaction forces. To scrutinize the BWS strategies, loss of dynamic similarity was calculated in terms of root mean square difference in gait dynamics (Delta gd) with respect to the reference gait under zero unloading. The gait dynamics were characterized by a dimensionless number Modela-w. Results SLIP model showed the lowest mrmse for 6 out of 8 gait parameters and for 1 other, the mrmse value were comparable to the MR model; SW model had the highest mrmse. Out of three BWS strategies, Tuned-Spring strategies led to the lowest Delta gd values. Conclusions The results of this work demonstrate the usefulness of gait models for BWS simulation and suggest the SLIP model to be more suitable for BWS simulations than the Simplest Walker and the Muscle-reflex models. Further, the Tuned-Spring approach appears to cause less distortions to the gait pattern than the more established Counterweight and Constant-Force approaches and merits experimental verification

Additional file 1 of Influence of body weight unloading on human gait characteristics: a systematic review

Chronological listing of the literature examined in this report, where Y: if the study used randomized trials and NA: nothing is mentioned explicitly about randomization of trials(NA). (XLSX 56.9 kb