Matching running footwear features to the functional needs of specific groups of runners stratified according to their training status or running experience has the potential to improve footwear preference, enhance running performance, and reduce the risk of running-related injuries. The complexity of modern running shoes in combination with the various functional needs of specific groups of runners, however, introduces many confounding factors, disguising what an appropriate shoe design should entail. As a result, it is crucial to identify and synthesise the effects of systematically modified running footwear features on lower extremity biomechanics, performance, injury risk, and footwear preference in runners of different skill levels to advance the knowledge of running biomechanics and footwear. The databases Scopus and PubMed were searched for related studies and 895 articles were identified. After the application of exclusion criteria, 68 articles remained and were arranged in a matrix that highlighted the investigated footwear feature and runner level. The effects of 20 footwear features on lower extremity biomechanics, injury, performance, and preference in novice, recreational, and high calibre runners were synthesised. Laced uppers with harder midsole materials in the forefoot/lateral regions of the shoe and softer materials in the rearfoot/medial regions increased comfort and thus likely preference. To increase performance, footwear should be constructed as lightweight as possible, and implement a range of different bending stiffness for individual needs. Injury risks at the knee joint were reduced in footwear with thinner midsoles. For high calibre runners, specifically, insoles should be custom moulded to the shape of the foot, and special attention needs to be payed to the elasticity of the upper, in order to avoid detrimental shifts in running patterns.

Ground reaction forces (GRFs) describe how runners interact with their surroundings and provide the basis for computing inverse dynamics. Wearable technology can predict time−continuous GRFs during walking and running; however, the majority of GRF predictions examine level ground locomotion. The purpose of this manuscript was to predict vertical and anterior–posterior GRFs across different speeds and slopes. Eighteen recreationally active subjects ran on an instrumented treadmill while we collected GRFs and plantar pressure. Subjects ran on level ground at 2.6, 3.0, 3.4, and 3.8 m/s, six degrees inclined at 2.6, 2.8, and 3.0 m/s, and six degrees declined at 2.6, 2.8, 3.0, and 3.4 m/s. We estimated GRFs using a set of linear models and a recurrent neural network, which used speed, slope, and plantar pressure as inputs. We also tested eliminating speed and slope as inputs. The recurrent neural network outperformed the linear model across all conditions, especially with the prediction of anterior–posterior GRFs. Eliminating speed and slope as model inputs had little effect on performance. We also demonstrate that subject−specific model training can reduce errors from 8% to 3%. With such low errors, researchers can use these wearable−based GRFs to understand running performance or injuries in real−world settings.

Human gait is as unique to an individual as is their fingerprint. It remains unknown, however, what gait characteristics differentiate well between individuals that could define the uniqueness of human gait. The purpose of this work was to determine the gait characteristics that were most relevant for a neural network to identify individuals based on their running patterns. An artificial neural network was trained to recognize kinetic and kinematic movement trajectories of overground running from 50 healthy novice runners (males and females). Using layer-wise relevance propagation, the contribution of each variable to the classification result of the neural network was determined. It was found that gait characteristics of the coronal and transverse plane as well as medio-lateral ground reaction forces provided more information for subject identification than gait characteristics of the sagittal plane and ground reaction forces in vertical or anterior-posterior direction. Additionally, gait characteristics during the early stance were more relevant for gait recognition than those of the mid and late stance phase. It was concluded that the uniqueness of human gait is predominantly encoded in movements of the coronal and transverse plane during early stance.

Footwear science research has seen a roughly 10-fold increase in publications over the last 20 years. This commentary will focus on the three primary research themes of this time frame: methodological developments, running-related injuries, and performance. Within each theme, we summarise the knowledge generated through the substantial increase in publications over the last couple of decades. The methodological developments highlight both improvements in data analysis techniques as well as changes in how we measure variables of interest. Running-related injury prediction paradigms have evolved significantly during these years, which affect how we recommend moving forward in the field. Substantial excitement has filled the performance research field, as we discuss how the advent of Advanced Footwear Technologies altered the research questions and approaches. The undeniable growth in the field over in recent years can be attributed to a strong foundation of knowledge, nurtured by a curiosity to obtain understanding through holistic approaches. The community has embarked on the next stage of the journey, armed with new data collection tools and analytical methodologies, with the objective to better understand the effect of novel footwear design on performance enhancement and injury prevention.

Studies on the paradigm of the preferred movement path are scarce, and as a result, many aspects of the paradigm remain elusive. It remains unknown, for instance, how muscle activity adapts when differences in joint kinematics, due to altered running conditions, are of low / high magnitudes. Therefore, the purpose of this work was to investigate changes in muscle activity of the lower extremities in runners with minimal (≤ 3°) or substantial (> 3°) mean absolute differences in the ankle and knee joint angle trajectories when subjected to different running footwear. Mean absolute differences in the integral of the muscle activity were quantified for the tibialis anterior (TA), peroneus longus (PL), gastrocnemius medialis (GM), soleus (SO), vastus lateralis (VL), and biceps femoris (BF) muscles during over ground running. In runners with minimal changes in 3D joint angle trajectories (≤ 3°), muscle activity was found to change drastically when comparing barefoot to shod running (TA: 35%; PL: 11%; GM: 17%; SO: 10%; VL: 27%; BF: 16%), and minimally when comparing shod to shod running (TA: 10%; PL: 9%; GM: 13%; SO: 8%; VL: 8%; BF: 12%). For runners who showed substantial changes in joint angle trajectories (> 3°), muscle activity changed drastically in barefoot to shod comparisons (TA: 39%; PL: 14%; GM: 16%; SO: 16%; VL: 25%; BF: 24%). It was concluded that a movement path can be maintained with small adaptations in muscle activation when running conditions are similar, while large adaptations in muscle activation are needed when running conditions are substantially different.

Human movement patterns were shown to be as unique to individuals as their fingerprints. However, some movement characteristics are more important than other characteristics for machine learning algorithms to distinguish between individuals. Here, we explored the idea that movement patterns contain unique characteristics that differentiate between individuals and generic characteristics that do not differentiate between individuals. Layer-wise relevance propagation was applied to an artificial neural network that was trained to recognize 20 male triathletes based on their respective movement patterns to derive characteristics of high/low importance for human recognition. The similarity between movement patterns that were defined exclusively through characteristics of high/low importance was then evaluated for all participants in a pairwise fashion. We found that movement patterns of triathletes overlapped minimally when they were defined by variables that were very important for a neural network to distinguish between individuals. The movement patterns overlapped substantially when defined through less important characteristics. We concluded that the unique movement characteristics of elite runners were predominantly sagittal plane movements of the spine and lower extremities during mid-stance and mid-swing, while the generic movement characteristics were sagittal plane movements of the spine during early and late stance.