Abstract
The identification of movement strategies in situations that are as ecologically valid as possible is essential for the understanding of lower limb interactions. This study considered the kinetic and kinematic data for the hip, knee and ankle joints from 376 block jump-landings when moving in the dominant and non-dominant directions from fourteen senior national female volleyball players. Two Machine Learning methods were used to generate the models from the dataset, Random Forest and Artificial Neural Networks. In addition, decision trees were used to detect which variables were relevant to discern the limb movement strategies and to provide a meaningful prediction. The results showed statistically significant differences when comparing the movement strategies between limb role (accuracy > 88.0% and > 89.3%, respectively), and when moving in the different directions but performing the same role (accuracy > 92.3% and > 91.2%, respectively). This highlights the importance of considering limb dominance, limb role and direction of movement during block jump-landings in the identification of which biomechanical variables are the most influential in the movement strategies. Moreover, Machine Learning allows the exploration of how the joints of both limbs interact during sporting tasks, which could provide a greater understanding and identification of risky movements and preventative strategies. All these detailed and valuable descriptions could provide relevant information about how to improve the performance of the players and how to plan trainings in order to avoid an overload that could lead to risk of injury. This highlights that, there is a necessity to consider the learning models, in which the spike approach unilaterally is taught before the block approach (bilaterally). Therefore, we support the idea of teaching bilateral approach before learning the spike, in order to improve coordination and to avoid asymmetries between limbs.
Highlights
The identification of movement strategies in situations that are as ecologically valid as possible is essential for the understanding of lower limb interactions
When a volleyball player is performing a block jump-landing efficiently, they move into tibial internal rotation which can lead to increased knee abduction and greater anterior cruciate ligament (ACL) loading[10]
Cust et al.[23] demonstrated the capacity of such Machine and Deep Learning methods to improve the understanding of sport movements and skill recognition, and how this can be applied to performance analysis to automate sport-specific movement recognition[23]. This current study explores the use of two Machine Learning methods: Artificial Neural Networks (ANN)[24] and Random Forest (RF)[25], with the aim to classify conditions for the directions of movement and limb role using kinematic and kinetic data, and decision trees to determine which variables were relevant to discern any differences in limb movement strategies
Summary
The identification of movement strategies in situations that are as ecologically valid as possible is essential for the understanding of lower limb interactions. Machine Learning allows the exploration of how the joints of both limbs interact during sporting tasks, which could provide a greater understanding and identification of risky movements and preventative strategies All these detailed and valuable descriptions could provide relevant information about how to improve the performance of the players and how to plan trainings in order to avoid an overload that could lead to risk of injury. The direction of the block jump-landing will vary within the game situation, resulting in a change to their normal three-step sequence when moving to the non-dominant direction, which in turn will affect the jump-landing movement strategy This can produce different limb movement strategies during jump-landing, and subsequently highlights possible asymmetries in strength and b alance[2]. Lobietti et al.[20] highlighted the importance of standardizing conditions including; directions, distance, and height of the jumps so that players land in a manner closer to that seen during a competition
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