BackgroundResilience of midsole material and the upper structure of the shoe are conceptual characteristics that can interfere in running biomechanics patterns. Artificial intelligence techniques can capture features from the entire waveform, adding new perspective for biomechanical analysis. This study tested the influence of shoe midsole resilience and upper structure on running kinematics and kinetics of non-professional runners by using feature selection, information gain, and artificial neural network analysis.MethodsTwenty-seven experienced male runners (63 ± 44 km/week run) ran in four-shoe design that combined two resilience-cushioning materials (low and high) and two uppers (minimalist and structured). Kinematic data was acquired by six infrared cameras at 300 Hz, and ground reaction forces were acquired by two force plates at 1,200 Hz. We conducted a Machine Learning analysis to identify features from the complete kinematic and kinetic time series and from 42 discrete variables that had better discriminate the four shoes studied. For that analysis, we built an input data matrix of dimensions 1,080 (10 trials × 4 shoes × 27 subjects) × 1,254 (3 joints × 3 planes of movement × 101 data points + 3 vectors forces × 101 data points + 42 discrete calculated kinetic and kinematic features).ResultsThe applied feature selection by information gain and artificial neural networks successfully differentiated the two resilience materials using 200(16%) biomechanical variables with an accuracy of 84.8% by detecting alterations of running biomechanics, and the two upper structures with an accuracy of 93.9%.DiscussionThe discrimination of midsole resilience resulted in lower accuracy levels than did the discrimination of the shoe uppers. In both cases, the ground reaction forces were among the 25 most relevant features. The resilience of the cushioning material caused significant effects on initial heel impact, while the effects of different uppers were distributed along the stance phase of running. Biomechanical changes due to shoe midsole resilience seemed to be subject-dependent, while those due to upper structure seemed to be subject-independent.

The judgement of skill experience and its levels is ambiguous though it is crucial for decision-making in sport sciences studies. We developed a fuzzy decision support system to classify experience of non-elite distance runners. Two Mamdani subsystems were developed based on expert running coaches’ knowledge. In the first subsystem, the linguistic variables of training frequency and volume were combined and the output defined the quality of running practice. The second subsystem yielded the level of running experience from the combination of the first subsystem output with the number of competitions and practice time. The model results were highly consistent with the judgment of three expert running coaches (r>0.88, p<0.001) and also with five other expert running coaches (r>0.86, p<0.001). From the expert’s knowledge and the fuzzy model, running experience is beyond the so-called "10-year rule" and depends not only on practice time, but on the quality of practice (training volume and frequency) and participation in competitions. The fuzzy rule-based model was very reliable, valid, deals with the marked ambiguities inherent in the judgment of experience and has potential applications in research, sports training, and clinical settings.

No abstract

Background: The progression of diabetes and the challenge of daily tasks may result in changes in biomechanical strategies.

Na prática fisioterapêutica, pressupõe-se que alterações posturais do membro inferior influenciem a biomecânica e função dos demais complexos durante o movimento. No entanto, a literatura sobre a relação entre desalinhamentos estáticos do joelho e possíveis alterações dinâmicas ainda é escassa e inconclusiva. Assim, buscou-se avaliar o efeito do alinhamento frontal estático do joelho sobre a distribuição da pressão plantar durante a marcha. Foram avaliados inicialmente 44 adultos jovens assintomáticos. Por fotogrametria digital, mediu-se o ângulo frontal do joelho, classificado como normal (170° a 175°), valgo <170º e varo >175°. Dado o baixo número de valgos, foram analisados dois grupos: de joelhos normais (n=18) e de joelhos varos (n=23). A distribuição da pressão plantar foi avaliada durante a marcha em cinco áreas. Os grupos mostraram-se estatistica-mente semelhantes em todas as variáveis cinéticas avaliadas em todas as áreas plantares. Joelhos normais apresentaram significativa correlação com o tempo de contato no antepé lateral e médio-pé; e os varos, correlação com a área e tempo de contato em duas e três áreas plantares, e com a pressão integral no antepé lateral. Os resultados mostraram que o desalinhamento frontal de 3° do joelho, embora com moderada correlação, não influencia a distribuição de cargas na superfície plantar durante a marcha. Sugere-se pois que a avaliação clínica não se limite à avaliação articular estática do joelho, mas inclua atividades dinâmicas.