Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach

The Influence of Materials on Footwear Behaviour: A Finite Element Simulation Study

The objective of this study was to analyse the influence of materials and their position within the upper assembly on the behaviour of casual footwear using finite element simulation tools. The study was carried out on three models of casual footwear, which are identical in terms of design lines, varying only in the materials of the upper assembly, namely calfskin leather (M1), knitted fabric (M2), and combination of knitted fabric and calfskin leather (M3). The footwear models were designed according to the design constraints specific to casual footwear. The foot was reconstructed based on the shoe last obtained based on anthropometric data. Material definition, 3D models editing, setting up analysis conditions, and constraints were performed using the Ansys 17.2 software. Gait biomechanics were taken into account to define the loading model, force distribution, force values, and constraints. The study evaluates footwear behaviour in terms of directional deformation (Z axis), equivalent von Mises stress, and equivalent elastic strain distribution. This paper explores a methodology that has the potential to enhance the footwear design and manufacturing process, providing designers with information about the deformations and stress distribution on upper parts of the footwear product.

Towards functionally individualised designed footwear recommendation for overuse injury prevention: a scoping review

Injury prevention is essential in running due to the risk of overuse injury development. Tailoring running shoes to individual needs may be a promising strategy to reduce this risk. Novel manufacturing processes allow the production of individualised running shoes that incorporate features that meet individual biomechanical and experiential needs. However, specific ways to individualise footwear to reduce injury risk are poorly understood. Therefore, this scoping review provides an overview of (1) footwear design features that have the potential for individualisation; and (2) the literature on the differential responses to footwear design features between selected groups of individuals. These purposes focus exclusively on reducing the risk of overuse injuries. We included studies in the English language on adults that analysed: (1) potential interaction effects between footwear design features and subgroups of runners or covariates (e.g., age, sex) for running-related biomechanical risk factors or injury incidences; (2) footwear comfort perception for a systematically modified footwear design feature. Most of the included articles (n = 107) analysed male runners. Female runners may be more susceptible to footwear-induced changes and overuse injury development; future research should target more heterogonous sampling. Several footwear design features (e.g., midsole characteristics, upper, outsole profile) show potential for individualisation. However, the literature addressing individualised footwear solutions and the potential to reduce biomechanical risk factors is limited. Future studies should leverage more extensive data collections considering relevant covariates and subgroups while systematically modifying isolated footwear design features to inform footwear individualisation.

Gender differences in footwear characteristics between half and full marathons in China: a cross-sectional survey

There are concerns about the risk of injuries caused by marathons in China. Since male and female runners have different injury risks, gender differences in running shoe functionality should be further complemented. A supervised questionnaire survey of 626 marathon runners was collected. The questionnaire was categorized into four sections: (1) participant profile, (2) importance of shoe properties, (3) functional evaluation of shoe properties and (4) importance ranking of shoe properties. The Mann-Whitney U test, Fisher's exact test of cross tabulation and Chi-square test, and two-way ANOVA were used to analyze the results of this survey. The significance level was set at P < 0.05. The full marathon participants were older than the half marathon participants. There was no gender difference in the importance of shoe features to elite runners. In addition, women are more concerned about upper elasticity and have higher requirements for running shoes than men. Women were more focused on injury prevention, while men were more focused on running performance. Heel cushioning was identified by all participants as the most important running shoe feature. There were no gender differences between elite players' demand for running shoes, but significant gender differences were found between genders at other running levels.

A Survey of Human Gait-Based Artificial Intelligence Applications

We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.

Acute effect of engineered thermoplastic polyurethane elastomer knockoff running footwear on foot loading and comfort during heel-to-toe running

BACKGROUND

Better midsole materials and comfort have been incorporated into more expensive shoes and are popular with runners. Consequently, knockoff running shoes are currently widely distributed in the Chinese market and and cost only 30%-50% of the total price of genuine branded products.

RESEARCH QUESTION

Uncertainty exists concerning the beneficial effects of advanced shoe material application in decreasing foot loading or impact force during running. Additionally, using comfort as a criterion to identify genuine branded running shoes may exclude brand factor.

METHODS

Fifteen healthy male volunteers were asked to perform two different tests, including running and a comfort evaluation. Each participant was asked to identify which footwear was the Adidas brand shoe based on their perception of comfort.

RESULTS

Time to the first peak of the vertical ground reaction force occurred significantly later when subjects wore the genuine branded shoe compared to knockoff shoe 1 (p = 0.003) and knockoff shoe 2 (p = 0.015) footwea. The genuine branded shoe (p = 0.005) and knockoff shoe 1 (p = 0.029) were significantly more comfortable compared to the knockoff shoe 2. Only four subjects selected the genuine branded shoe, whereas six subjects selected both the genuine branded shoe and knockoff shoe 1.

SIGNIFICANCE

Knockoff running footwear significantly increases impact loading compared to the genuine branded product, thereby posing greater risk of running injury.

Systematic Review of the Role of Footwear Constructions in Running Biomechanics: Implications for Running-Related Injury and Performance

Although the role of shoe constructions on running injury and performance has been widely investigated, systematic reviews on the shoe construction effects on running biomechanics were rarely reported. Therefore, this review focuses on the relevant research studies examining the biomechanical effect of running shoe constructions on reducing running-related injury and optimising performance. Searches of five databases and Footwear Science from January 1994 to September 2018 for related biomechanical studies which investigated running footwear constructions yielded a total of 1260 articles. After duplications were removed and exclusion criteria applied to the titles, abstracts and full text, 63 studies remained and categorised into following constructions: (a) shoe lace, (b) midsole, (c) heel flare, (d) heel-toe drop, (e) minimalist shoes, (f) Masai Barefoot Technologies, (g) heel cup, (h) upper, and (i) bending stiffness. Some running shoe constructions positively affect athletic performance-related and injury-related variables: 1) increasing the stiffness of running shoes at the optimal range can benefit performance-related variables; 2) softer midsoles can reduce impact forces and loading rates; 3) thicker midsoles can provide better cushioning effects and attenuate shock during impacts but may also decrease plantar sensations of a foot; 4) minimalist shoes can improve running economy and increase the cross-sectional area and stiffness of Achilles tendon but it would increase the metatarsophalangeal and ankle joint loading compared to the conventional shoes. While shoe constructions can effectively influence running biomechanics, research on some constructions including shoe lace, heel flare, heel-toe drop, Masai Barefoot Technologies, heel cup, and upper requires further investigation before a viable scientific guideline can be made. Future research is also needed to develop standard testing protocols to determine the optimal stiffness, thickness, and heel-toe drop of running shoes to optimise performance-related variables and prevent running-related injuries.

Three-dimensional motion capture data during repetitive overarm throwing practice

Three-dimensional motion capture analysis is considered the gold standard for any movement research. Motion capture data were recorded for 7 healthy female participants with no prior throwing experience to investigate the learning process for overarm throwing during a selected period. Participants were monitored 3 times a week for 5 weeks. Each session consisted of 15 dominant and 15 nondominant hand side overarm throws. A total of 3,150 trials were recorded and preprocessed (labeling reflective markers) for further analysis. The presented dataset can provide valuable information about upper extremity kinematics of the learning process of overarm throwing without any kind of feedback. Furthermore, this dataset may be used for more advanced analysis techniques, which could lead to more insightful information.