Recognition of Foot-Ankle Movement Patterns in Long-Distance Runners With Different Experience Levels Using Support Vector Machines.

Running practice could generate musculoskeletal adaptations that modify the body mechanics and generate different biomechanical patterns for individuals with distinct levels of experience. Therefore, the aim of this study was to investigate whether foot-ankle kinetic and kinematic patterns can be used to discriminate different levels of experience in running practice of recreational runners using a machine learning approach. Seventy-eight long-distance runners (40.7 ± 7.0 years) were classified into less experienced (n = 24), moderately experienced (n = 23), or experienced (n = 31) runners using a fuzzy classification system, based on training frequency, volume, competitions and practice time. Three-dimensional kinematics of the foot-ankle and ground reaction forces (GRF) were acquired while the subjects ran on an instrumented treadmill at a self-selected speed (9.5-10.5 km/h). The foot-ankle kinematic and kinetic time series underwent a principal component analysis for data reduction, and combined with the discrete GRF variables to serve as inputs in a support vector machine (SVM), to determine if the groups could be distinguished between them in a one-vs.-all approach. The SVM models successfully classified all experience groups with significant crossvalidated accuracy rates and strong to very strong Matthew's correlation coefficients, based on features from the input data. Overall, foot mechanics was different according to running experience level. The main distinguishing kinematic factors for the less experienced group were a greater dorsiflexion of the first metatarsophalangeal joint and a larger plantarflexion angles between the calcaneus and metatarsals, whereas the experienced runners displayed the opposite pattern for the same joints. As for the moderately experienced runners, although they were successfully classified, they did not present a visually identifiable running pattern, and seem to be an intermediate group between the less and more experienced runners. The results of this study have the potential to assist the development of training programs targeting improvement in performance and rehabilitation protocols for preventing injuries.

Impact of diabetic neuropathy severity on foot clearance complexity and variability during walking.

BACKGROUND: The control of foot trajectory during swing phase is important to achieve safe clearance with the ground. Complexity of a physiological control system arises from the interaction of structural units and regulatory feedback loops that operate to enable the organism to adapt to a non-static environment. Diabetic polyneuropathy (DPN) impairs peripheral feedback inputs and alters ankle control during gait, which might affect toe clearance (ToC) parameters and its complexity, predisposing DPN-subjects to tripping and falling. RESEARCH QUESTION: How do different DPN-severity degrees change ToC trajectory and minimum ToC, and its complexity during gait of diabetic subjects? METHODS: 15 healthy controls and 69 diabetic subjects were assessed and classified into DPN-severity degrees by an expert fuzzy model: absent (n = 26), mild (n = 21) and severe (n = 22). Three-dimensional kinematics was measured during comfortable walking. ToC was the minimum vertical distance between the marker placed at the first metatarsal head and the ground during swing. Mean ToC, ToC standard deviation (SD) between trials, and sample entropy (SaEn) and standard deviation (SD) of ToC trajectory were calculated from the ToC temporal series. ANOVA and ANCOVA (with the walking speed as the covariate) and Bonferroni pairwise post-hoc tests (P < 0.05) were used to compare groups. RESULTS: Mean ToC and ToC SD did not show differences between groups (ANCOVA F = 0.436; df = 3; P = 0.705; F=1.719; df=3; P=0.170, respectively). ToC trajectory SD also did not show differences between groups (ANCOVA F = 3.98; df = 3; P = 0.755). Severe-DPN subjects showed higher ToC_Traj_SaEn than controls (ANCOVA F=2.60; df=3; P = 0.05). SIGNIFICANCE: Severe-DPN subjects showed a more complex pattern of overall foot-ankle trajectory in swing phase in comparison to controls, although did not present lower minimum ToC values. The higher complexity of ToC might lead to an increase in the motor system output (more strategies, increase in variability), resulting in a more unstable system and selected motor strategies.