Three-dimensional data-tracking simulations of sprinting using a direct collocation optimal control approach

Abstract

Biomechanical simulation and modelling approaches have the possibility to make a meaningful impact within applied sports settings, such as sprinting. However, for this to be realised, such approaches must first undergo a thorough quantitative evaluation against experimental data. We developed a musculoskeletal modelling and simulation framework for sprinting, with the objective to evaluate its ability to reproduce experimental kinematics and kinetics data for different sprinting phases. This was achieved by performing a series of data-tracking calibration (individual and simultaneous) and validation simulations, that also featured the generation of dynamically consistent simulated outputs and the determination of foot-ground contact model parameters. The simulated values from the calibration simulations were found to be in close agreement with the corresponding experimental data, particularly for the kinematics (average root mean squared differences (RMSDs) less than 1.0° and 0.2 cm for the rotational and translational kinematics, respectively) and ground reaction force (highest average percentage RMSD of 8.1%). Minimal differences in tracking performance were observed when concurrently determining the foot-ground contact model parameters from each of the individual or simultaneous calibration simulations. The validation simulation yielded results that were comparable (RMSDs less than 1.0° and 0.3 cm for the rotational and translational kinematics, respectively) to those obtained from the calibration simulations. This study demonstrated the suitability of the proposed framework for performing future predictive simulations of sprinting, and gives confidence in its use to assess the cause-effect relationships of technique modification in relation to performance. Furthermore, this is the first study to provide dynamically consistent three-dimensional muscle-driven simulations of sprinting across different phases.

Modifications to the net knee moments lead to the greatest improvements in accelerative sprinting performance: a predictive simulation study

“…The three-dimensional full-body musculoskeletal model described in Haralabidis et al 20 was used to perform the simulations in this study (Fig. 2 ).…”

“…This was mainly due to the challenges in creating both a valid model and a robust simulation framework to realistically investigate such a demanding activity. In this study we tackled this challenge and performed predictive simulations of accelerative sprinting to investigate how modifications in technique affected performance, using a previously validated framework 20 . The secondary aim of this investigation was to provide insights into the front-side mechanics coaching framework, and to investigate whether the technique modifications coincided with improvements in performance as explained by the front-side mechanics coaching framework.…”

Research and Implementation of the Sports Analysis System Based on 3D Image Technology

“…Haralabidis et al [17] described the structure of the human body with simple shapes and searched for the closest human body region in the input image to determine the body region. This process is the process of energy equation optimization.…”

“…A sports model system based on three-dimensional images usually includes three parts, namely, a transmitting source, a receiving sensor, and a data processing unit. The emission source is used to generate an electromagnetic field, and the distribution of the electromagnetic field is regular; the key parts of the athlete's body need to be equipped with receiving sensors (usually [10][11][12][13][14][15][16][17][18][19][20], and the connection between the sensors and the data processing unit is achieved through cables. In order to reduce interference and jitter, the sampling rate of this type of system is generally lower than 15 Hz, and for some high-speed sports such as football and basketball, such a sampling rate is far from meeting the requirements.…”

Change the direction: 3D optimal control simulation by directly tracking marker and ground reaction force data

“…Reconstructive optimal control simulations are traditionally generated by tracking generalized coordinates or joint angles in combination with measured GRFs (e.g., Dembia et al (2020); Haralabidis et al (2021); Heinrich et al (2014); Nitschke et al (2020); van den Bogert et al (2011, 2012); see Fig. 1b).…”