Machine learning (ML) techniques such as (deep) artificial neural networks (DNN) are solving very successfully a plethora of tasks and provide new predictive models for complex physical, chemical, biological and social systems. However, in most cases this comes with the disadvantage of acting as a black box, rarely providing information about what made them arrive at a particular prediction. This black box aspect of ML techniques can be problematic especially in medical diagnoses, so far hampering a clinical acceptance. The present paper studies the uniqueness of individual gait patterns in clinical biomechanics using DNNs. By attributing portions of the model predictions back to the input variables (ground reaction forces and full-body joint angles), the Layer-Wise Relevance Propagation (LRP) technique reliably demonstrates which variables at what time windows of the gait cycle are most relevant for the characterisation of gait patterns from a certain individual. By measuring the time-resolved contribution of each input variable to the prediction of ML techniques such as DNNs, our method describes the first general framework that enables to understand and interpret non-linear ML methods in (biomechanical) gait analysis and thereby supplies a powerful tool for analysis, diagnosis and treatment of human gait.

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Traditional learning approaches are typically based on a linear understanding of causality where the same cause leads to the same effect. In recent years there has been increasing interest in the complexity of nature and living phenomena, with significant insights provided by models of change that are based on a nonlinear understanding of causality, where small causes can lead to big effects and vice versa. In this vein, learning processes seem to be more successful for inducing behavioral change when teaching processes deviate from a linear approach. The differential learning approach takes advantage of fluctuations in a complex system by increasing them through 'no repetition' and 'constantly changing movement tasks' which add stochastic perturbations. Previous research has provided much evidence on the superiority of a differential learning approach for learning single movement techniques, in comparison to repetition-and correctionoriented approaches. In this pilot study, the parallel acquisition and learning of two movement techniques in the sport of football are the objective of investigation. One traditionally trained group and two differentially trained groups (blocked and random) trained for 4 weeks, twice a week, on ball control and shooting at goal tasks. Results supported previous work and revealed significant advantages for both differential groups in the acquisition phase as well as in the learning phase, compared to the traditional group. These data suggest that, instead of following a direct linear path towards the target of a 'to-be-learned' movement technique by means of numerous repetitions and corrections, a differential approach is more beneficial because it perturbs learners towards more functional movement patterns during practice.Keywords: Differential Learning, complex systems, fluctuations, football, movement variability. INTRODUCTIONTraditional models of learning have recently been questioned because of their principles that all learners typically start with the same exercise followed by other identical teaching exercises in order to build up a methodical sequence of exercises followed by all students in order to achieve stipulated learning goals [1]. A similar logic underpins the interpretation of traditional pedagogical principles that all learners need to progress "from easy to hard" or "from simple to complex" exercises. In principle this logic implies the understanding of linear causality as fundamental basis for a linear pedagogy. In a weak version of this approach to learning, linear causality assumes that same causes will lead to same effects. In the strong version (because much more mathematical conditions have to be fulfilled) similar causes will lead to similar effects. In reality these assumptions are associated with models of linear equations in which the result is just a sum of weighted parameters of influence. In practice this approach is accompanied by the breaking up of a sports movement into certain phases or anatomical focuses that are all trained separately...

The differential-learning program stressed creative and positional behavior in both age groups with a distinct magnitude of effects, with the U13 players demonstrating higher improvements over the U15 players. Overall, these findings confirmed that the technical variability promoted by differential learning nurtures regularity of positioning behavior.

The aim of this study was to identify the effects of a complementary training program based on differential learning approach in the physical, technical, creative and positioning performance of youth football attackers. Fifteen players were allocated into the control (U15C = 9, age: 13.9±0.5 years; U17C = 6, age: 16.1±0.7 years) and the experimental (U15E = 9, age: 14.2±0.8 years; U17E = 6, age: 15.8±0.5 years) groups. The experimental groups participated in 10-weeks of a complementary training program based on differential learning approach to improve physical literacy and players’ tactical behavior. Variables studied encompassed: motor (vertical jump, speed and repeated change-of direction), technical (pass, dribble and shot), creative (fluency, attempts, versatility) and positioning-related variables (stretch index, spatial exploration index and regularity of the lateral and longitudinal movements). Results revealed that U15E improved both the jump and repeated change-of-direction performance, while the U17E have only improved the jump performance. The U15E showed improvements in all technical variables (small to large effects), and in the fluency and versatility (moderate effects), while the U17 have only improved the successful shots (large effects). From a positional perspective, there was a moderate increase in the stretch index, and decreased longitudinal and lateral regularity (small to moderate effects) in the U15E compared to the U15C. In turn, the U17E revealed a moderate increase of the spatial exploration index and a small decrease in the stretch index. Overall, the results suggest that the complementary training program was effective for the development of the overall performance of the U15E attackers, while more time and/or variability may be needed for older age groups. Nevertheless, the overall higher values found in experimental groups, may suggest that this type of complementary training program improves performance.