La prédiction des blessures en sport : fiction ou réalité ?

peer reviewedToday, injury in sport represents a major problem for athletes and their entourage. Prevention measures are developed and are available to the sports community. Among them, the emergence of new technologies and data analysis approaches offer new opportunities. Given the fact that these prediction methods tend to develop, it seemed important to us that the actors around the athlete, and in particular health professionals, have notions to better understand these approaches and to be able to interpret the work presenting injury prediction (risk estimation) analyses. Through this article, based on a narrative review of the literature, we have presented Machine Learning (ML), as well as its applications and limitations. ML, or “machine learning”, is a tool derived from statistics, related to artificial intelligence, which makes it possible to build, from input data (predictive variables) and output data (variables to be predicted), models capable of predicting an event. Thus, like any analysis, ML can present certain limitations and risks that should be avoided, but also to know and detect when reading articles/works using ML, or when you want to use it. In conclusion, in sports traumatology, Machine Learning models offer the opportunity: 1) to help diagnose injuries or; 2) to optimize athletes’ training by estimating their risk of injury, both in a screening and monitoring context. However, this prediction tool cannot adapt to all situations without risk and can sometimes lead to false predictions. Thus, Machine Learning offers interesting perspectives with the possibility of having a decision support tool for field actors, but it is necessary to take into account the limits and risks of this approach in order to use them best and get the best benefits. Machine Learning is not a crystal ball that allows us to see the future, but a method of data analysis that relies on measured data and therefore depends on the quality of the latter

Innovative structure design for impact energy absorption and dissipation : road safety domain

La sécurité routière, et en particulier la réduction des accidents graves liés à un choc entre un véhicule et un obstacle fixe, est un enjeu sociétal important. La majeure partie du réseau routier est équipée de dispositifs de sécurité tels que des barrières ou des atténuateurs de choc. Ces derniers absorbent l’énergie cinétique des voitures accidentées principalement par déformation plastique et peuvent s’étendre sur plusieurs mètres. Réduire, à la fois, les dimensions de ces dispositifs et la décélération ressentie par les usagers du véhicule accidenté est un enjeu important qui peut conduire à la réduction du nombre d’accidents mortels. Dans le but de résoudre cette problématique, un processus d’optimisation est appliqué à des structures architecturées, telles que les nids d’abeille, numériquement modélisées dans des conditions d’impact. Une nouvelle fonction objectif, fondée sur la norme européenne EN1317, a été développée dans le but d’améliorer les capacités d’absorption des atténuateurs de choc tout en évitant les pics de décélération. Le processus global d’optimisation a été mené à l’aide de l’algorithme d’optimisation méta-heuristique Inverse-PageRank-PSO, sur un modèle éléments finis validé par des essais expérimentaux. L’algorithme a conduit à la configuration optimisée de nids d’abeille améliorant les performances des atténuateurs de choc actuels. Les quatre structures optimisées présentent une courbe d’absorption d’énergie cinétique quasi-linéaire, comme recommandé par la norme européenne, avec une réduction de leur dimension de 25% dans la direction d’impact. De plus, une méthode de Machine Learning par krigeage, intitulée AptM, a été développée pour calibrer automatiquement les paramètres des algorithmes méta-heuristiques dans un contexte de résolution de problèmes d’optimisation. Les résultats numériques montrent que AptM permet une amélioration significative de la précision de convergence des méta-heuristiques. AptM a été validée sur un benchmark de fonctions mathématiques, puis appliquée à un problème d’optimisation de structures en treillis.Road safety and particularly the reduction of car crash accidents involving fixed obstacles is a societal concern. Most of the road networks are lined with many advanced road safety systems. These devices absorb the kinetic energy of crashing cars mostly by plastic deformation and can spread on several meters. Reducing the devices’ dimensions and the deceleration felt by the car crashed users is an important issue that can lead to a reduction of mortal car accidents. In order to solve this technical issue, an optimization process is applied to architectured structures, such as, honeycomb structures numerically modelled under car crash impact conditions. A new objective function based on the European standard has been developed in order to improve crash cushions capabilities while avoiding the peak deceleration felt by the car users by using a meta-heuristic optimization algorithm. The global optimization process has been performed by using the Inverse-PageRank-PSO algorithm applied on a FE model validated by experimental tests. The algorithm has led to an optimal configuration of honeycombs improving the performances of current road safety devices. The four optimal structures present a quasi-linear absorption curve, as recommended by European standards with a reduced size of 25% in the impact direction. Furthermore, an automatic kriging machine learning method, entitled AptM, has been developed in order to calibrate meta-heuristic algorithms parameters for solving efficiently lots of different optimization problems. The numerical results show that the AptM methodology allows a significant improvement of the convergence accuracy of meta-heuristics. AptM has been validated on a mathematical benchmark and then applied to truss structures optimization

Développement de structures innovantes destinées à absorber et dissiper l'énergie d'un choc : application à la sécurité routière

Road safety and particularly the reduction of car crash accidents involving fixed obstacles is a societal concern. Most of the road networks are lined with many advanced road safety systems. These devices absorb the kinetic energy of crashing cars mostly by plastic deformation and can spread on several meters. Reducing the devices’ dimensions and the deceleration felt by the car crashed users is an important issue that can lead to a reduction of mortal car accidents. In order to solve this technical issue, an optimization process is applied to architectured structures, such as, honeycomb structures numerically modelled under car crash impact conditions. A new objective function based on the European standard has been developed in order to improve crash cushions capabilities while avoiding the peak deceleration felt by the car users by using a meta-heuristic optimization algorithm. The global optimization process has been performed by using the Inverse-PageRank-PSO algorithm applied on a FE model validated by experimental tests. The algorithm has led to an optimal configuration of honeycombs improving the performances of current road safety devices. The four optimal structures present a quasi-linear absorption curve, as recommended by European standards with a reduced size of 25% in the impact direction. Furthermore, an automatic kriging machine learning method, entitled AptM, has been developed in order to calibrate meta-heuristic algorithms parameters for solving efficiently lots of different optimization problems. The numerical results show that the AptM methodology allows a significant improvement of the convergence accuracy of meta-heuristics. AptM has been validated on a mathematical benchmark and then applied to truss structures optimization.La sécurité routière, et en particulier la réduction des accidents graves liés à un choc entre un véhicule et un obstacle fixe, est un enjeu sociétal important. La majeure partie du réseau routier est équipée de dispositifs de sécurité tels que des barrières ou des atténuateurs de choc. Ces derniers absorbent l’énergie cinétique des voitures accidentées principalement par déformation plastique et peuvent s’étendre sur plusieurs mètres. Réduire, à la fois, les dimensions de ces dispositifs et la décélération ressentie par les usagers du véhicule accidenté est un enjeu important qui peut conduire à la réduction du nombre d’accidents mortels. Dans le but de résoudre cette problématique, un processus d’optimisation est appliqué à des structures architecturées, telles que les nids d’abeille, numériquement modélisées dans des conditions d’impact. Une nouvelle fonction objectif, fondée sur la norme européenne EN1317, a été développée dans le but d’améliorer les capacités d’absorption des atténuateurs de choc tout en évitant les pics de décélération. Le processus global d’optimisation a été mené à l’aide de l’algorithme d’optimisation méta-heuristique Inverse-PageRank-PSO, sur un modèle éléments finis validé par des essais expérimentaux. L’algorithme a conduit à la configuration optimisée de nids d’abeille améliorant les performances des atténuateurs de choc actuels. Les quatre structures optimisées présentent une courbe d’absorption d’énergie cinétique quasi-linéaire, comme recommandé par la norme européenne, avec une réduction de leur dimension de 25% dans la direction d’impact. De plus, une méthode de Machine Learning par krigeage, intitulée AptM, a été développée pour calibrer automatiquement les paramètres des algorithmes méta-heuristiques dans un contexte de résolution de problèmes d’optimisation. Les résultats numériques montrent que AptM permet une amélioration significative de la précision de convergence des méta-heuristiques. AptM a été validée sur un benchmark de fonctions mathématiques, puis appliquée à un problème d’optimisation de structures en treillis

An automatic kriging machine learning method to calibrate meta-heuristic algorithms for solving optimization problems

For years, meta-heuristic algorithms have been widely studied and many improved versions have been developed: from the evolution of the swarm topologies of the Particle Swarm Optimization algorithm, to the using of machine learning to Differential Evolutionary algorithms. However, the tuning of the fundamental meta-heuristic parameters has been less studied, but may lead to significant improvements on the convergence accuracy of these algorithms. This paper aims at developing an automated methodology to calibrate the parameters of population-based meta-heuristic algorithms for optimization problems. Based on the kriging estimation of the best combination of parameters, the Automated parameter tuning of Meta-heuristics (AptM) methodology gives the optimal algorithm setup for each considered problem in order to lead to a better convergence accuracy. The proposed AptM methodology is used to tune three different meta-heuristic algorithms, each applied to twelve mathematical unimodal or multimodal objective functions. AptM methodology performance is assessed by comparison of classical setups usually used in the literature. The numerical results show that the AptM methodology allows a significant improvement of the convergence accuracy of meta-heuristics with an average improvement of 62.02%, 69.12% and 64.94% on optimization problems defined in dimensions 10, 30 and 50 respectively. An experimental criterion is defined based on the convergence accuracy of the AptM methodology over the classical setups, assessing the AptM performances. The previous experimental criterion allows to compare the AptM methodology over the base-set. The AptM methodology shows a significant improvement of the algorithms performance on 97.2% of the tested problems

Perceptions and beliefs on sports injury prediction as an injury risk reduction strategy: An online survey on elite athletics (track and field) athletes, coaches, and health professionals

International audienc

Risk factors for injury complaints leading to restricted participation in Athletics (Track and Field): a secondary analysis of data from 320 athletes over one season

Objective To investigate if several potential risk factors were associated with time to injury complaints leading to participation restriction in Athletics (ICPR).Methods We performed a secondary analysis of data collected during 39 weeks of the 2017–2018 Athletics season in a cluster-randomised controlled trial (‘PREVATHLE’). Univariate and multivariable analyses using Cox regression models were performed to analyse the association between the time to first ICPR and potential risk factors collected (1) at baseline: sex, age, height, body mass, discipline, the usual duration of Athletics training and non-specific sports training, ICPR in the preceding season (yes/no), ICPR at baseline (yes/no); (2) weekly during the season: duration and intensity of Athletics training and competition, and non-specific sports training, fitness subjective state, sleep duration and illness (yes/no); and (3) combined.Results Data from 320 athletes were included; 138 (43.1%) athletes reported at least one ICPR during the study follow-up. The combined multivariable analyses revealed that the risk of ICPR at any given time was significantly higher in athletes with a pre-existing ICPR (hazard rate ratio, HRR 1.90, 95% CI 1.15 to 3.15; p=0.012) and lower in athletes with a higher fitness subjective state (HRR 0.63, 95% CI 0.55 to 0.73; p<0.001) and who had had at least one illness during the season (HRR 0.42, 95% CI 0.29 to 0.62; p<0.001).Conclusions Our results provide new insights into injury risk factors in Athletics that could help with potential injury risk reduction strategies. These could be to explore the pre-existing injury presence at the season’s beginning and to monitor the fitness subjective state and illnesses occurrence during the season.Trial registration ClinicalTrials.gov Identifier: NCT0330743