The study, published in the journal Sports Engineering, set out to answer a deceptively simple question: does a running shoe behave the same way when it is compressed straight down as it does when it is compressed at the angles produced by real foot strikes? Standard test methods, including the widely used ASTM F1614 protocol, prescribe uniaxial loading in which the shoe is squeezed perpendicular to its sole. Yet runners rarely land that way. Rearfoot strikers contact the ground heel-first with the sole inclined roughly 16 to 20 degrees above horizontal, while forefoot strikers land on the ball of the foot with the sole tilted about 15 to 19 degrees in the opposite direction. Those angular differences change which part of the midsole absorbs the load and how much foam is engaged.

To capture this, the team tested six women’s US size 8.5 running shoe constructions that differed in midsole material, hardness and stack height. Two commercial New Balance models, the 880 with a 34.5 millimetre stack and the 1080 with a 40.5 millimetre stack, provided the basis for constructions built on production tooling, meaning the midsoles were representative of retail footwear. The materials spanned the two foams that dominate the modern market: ethylene-vinyl acetate, or EVA, the long-standing workhorse valued for cushioning, durability and low cost, and polyether block amide, or PEBA, the high-resilience superfoam behind today’s performance racing shoes. PEBA was tested at two hardness levels, 37 and 50 Asker C, while EVA was available only at the harder 50 Asker C grade.

Each shoe was fitted over a rigid mechanical last shaped to a size 8.5 foot and mounted on an Instron ElectroPuls E3000 testing machine. The last could be rotated about its transverse axis, allowing the researchers to load the shoe at three orientations: 15 degrees of dorsiflexion to mimic a rearfoot strike, a neutral 0 degrees for a midfoot strike, and 15 degrees of plantarflexion for a forefoot strike. Every cycle ran from a 50 newton preload to a peak force of 1500 newtons, roughly 2.5 times the body weight of a 60 kilogram runner, applied as a sinusoidal waveform at 1.5 hertz under closed-loop force control. Because the waveform shape and peak force were held constant, orientation was the only loading variable that changed. Each shoe received 30 preconditioning cycles before testing, and 25 cycles were recorded at each orientation, with the first five discarded to let the foam settle after repositioning.

From the force-displacement data the researchers extracted three outcomes. Vertical stiffness was calculated as the gradient of the loading curve between 70 and 90 percent of peak force, a range that avoids the nonlinear toe region at low loads and the densification zone near maximum compression. Peak displacement was the compression recorded at peak force. Energy return percentage was the energy recovered during unloading, obtained by trapezoidal integration, expressed as a fraction of the energy absorbed during loading. After removing 17 outlying cycles, or 4.7 percent of the dataset, by an interquartile range rule confirmed by curve inspection, 343 observations remained for statistical modelling.

The results were striking in their consistency. In every single shoe construction, stiffness was lowest under dorsiflexion, the rearfoot-strike orientation, and peak displacement was highest under the same condition. Compared with neutral loading, dorsiflexion reduced stiffness by 72 newtons per millimetre in the material-comparison model and increased peak displacement by 8.6 millimetres. Plantarflexion produced the same pattern of softer, deeper compression but with smaller magnitudes, a 37 newton per millimetre stiffness reduction and 2.4 millimetres of extra displacement. In the second model, comparing soft and hard PEBA, the orientation effects were smaller for stiffness but nearly identical for displacement, at 8.3 and 2.3 millimetres. All of these effects were statistically significant.

The physical explanation lies in contact geometry. When the shoe is tilted relative to the applied load, the region and volume of foam engaged during compression change, reducing the amount of material available to support the force. With less foam doing the work, the midsole compresses further under the same load. Dorsiflexion produced the larger effect, which the authors attribute to the reduced contact area associated with angled loading on the curved rearfoot geometry. The findings extend earlier work by Mohammadi and Nourani, who reported that compression angle affected strain energy in EVA soles, and they align with the observation that rearfoot and forefoot strikers load different regions of the midsole during running. In other words, the orientation effects observed in the laboratory are mechanically relevant to what actually happens on the road.

Energy return told a different story. Of the three outcomes, it was the least sensitive to loading orientation, and most of its variance was explained by differences between shoe constructions rather than by the angle of loading or the number of cycles. In the material comparison, the contrast between EVA and PEBA accounted for a large share of the variance, with PEBA showing a non-significant trend toward higher energy return and significantly greater peak displacement. In the PEBA hardness comparison, hardness itself explained very little, suggesting that the differences between soft and hard PEBA shoes reflect features other than durometer, possibly including geometry or microstructure that the study did not characterise. This supports the researchers’ hypothesis that energy return, being rooted in material-level properties, would remain relatively stable across orientations.

The study also documented a subtle but important drift across the 20 analysed cycles. Peak displacement and energy return changed slightly and linearly with successive loading cycles, meaning that the value measured depends on how many cycles precede the measurement. A standard single-orientation, fixed-cycle test captures neither this cyclic drift nor the orientation sensitivity, so it may not represent the stiffness and displacement a runner actually experiences. The authors are careful to note that the standard test remains a useful benchmark, but they argue that a fuller characterisation of running-relevant loading requires more than one orientation and a stated cycle count. Because rearfoot cushioning properties are known to influence vertical ground reaction forces during heel-toe running, the orientation-dependent differences could carry biomechanical consequences in actual use.

The researchers acknowledge several limitations. Only one shoe was tested per material-hardness-stack-height combination, so manufacturing variability could not be assessed, and with just two constructions per group the between-group comparisons are exploratory. The constant 1500 newton peak force does not capture the variation in ground reaction forces across running speeds and body masses, the three tested orientations approximate but do not span the continuous spectrum of real foot strike angles, and each material was represented by a single density, so material and density effects could not be separated. Foam cell structure was not characterised, and stack height was compared between two shoe models whose other differences cannot be fully disentangled. Future work, the authors suggest, should test whether the orientation effects translate into measurable biomechanical changes during running, examine loading frequencies given the viscoelastic nature of midsole foams, extend cyclic testing to see whether orientation effects persist as foam ages, and replicate across multiple shoes of the same construction. For now, the message for the footwear industry is clear: a shoe tested only straight down may not behave the way it does when a runner lands on the heel or the forefoot, and multi-orientation testing offers a more honest picture of how midsoles actually cushion the millions of angled impacts that running delivers.

Subject of Research: Effects of loading orientation on the mechanical cushioning properties of running shoe midsoles

Article Title: Mechanical cushioning properties of running shoe constructions across loading orientations

Article References: Scherrer, D., Legg, K. A., Rogers, C. W., Gottschall, J. S., & Cochrane, D. J. (2026). Mechanical cushioning properties of running shoe constructions across loading orientations. Sports Engineering, 29(2), Article 33. https://doi.org/10.1007/s12283-026-00567-2

Image Credits: AI Generated

DOI: 10.1007/s12283-026-00567-2

Keywords: running shoes, midsole, cushioning, EVA, PEBA, foot strike, stiffness, energy return, biomechanics, mechanical testing, stack height, sports engineering

The review followed a rigorous scoping methodology aligned with the PRISMA Extension for Scoping Reviews and the Joanna Briggs Institute Population–Concept–Context framework. The team searched three engineering and computer science databases, Scopus, the ACM Digital Library and IEEE Xplore, for publications between January 2000 and February 2025. In a striking methodological decision, the researchers deliberately excluded the word rhythm itself, along with synonyms such as timing, tempo, flow and cadence, from their search strings. Because rhythm is conceptually complex and used differently across disciplines, searching for it directly would have flooded the results with irrelevant work. Instead, they captured the broad landscape of augmentation and feedback technologies in sport, then examined whether the holistic, expert-understood concept of rhythm was actually being addressed. After screening 442 unique records from an initial pool of 529, the team distilled the literature down to 129 technology-focused studies.

The first headline finding is the era of the wearable sensor. Wearable devices were the primary technology in 48.8 percent of the included studies, and inertial measurement units, the tiny accelerometer and gyroscope packages that detect motion, appeared in 44.2 percent of the entire corpus. Their dominance reflects maturity, low cost and high ecological validity: unlike laboratory motion capture rigs, IMU-based wearables can record rhythm-relevant features such as step frequency, stance duration and inter-movement intervals directly in the field, on the track, in the pool or on the slopes. Examples in the corpus range from flexible sensor networks for sprint and jump biomechanics to smart dumbbells with embedded inertial units that evaluate strength training form in real time. For anyone hoping to quantify the temporal texture of movement, the sensor hardware problem is, in essence, already solved.

The second headline is a turn toward seeing rather than feeling the data. Computer vision systems accounted for 20.2 percent of primary technologies and extended reality systems for 22.5 percent, together representing more than 40 percent of the corpus. Visual capture technologies spanning ordinary RGB cameras, LiDAR and full motion capture appeared in one third of all hardware implementations. This suggests a field diverging into two camps: one that attaches discrete sensors to the body, and another that leverages optics and immersive environments where users quite literally inhabit their own performance data. The rise of extended reality, in particular, signals a shift beyond passive data logging toward experiential interfaces, exemplified by virtual reality training systems evaluated with national-level sitting volleyball players that produced measurable skill improvement.

Underpinning both camps is artificial intelligence. Explicit use of AI or machine learning to generate or adapt feedback appeared in 42 percent of the included studies. Rather than being the primary technology, machine learning frequently serves as the translation layer that converts raw sensor streams into meaningful, actionable cues. Examples range from wearable systems using deep learning to provide adaptive auditory feedback for weight training to computer vision pipelines that assess landing form in basketball. In principle, this is exactly the kind of computational machinery needed to move from simple real-time alerts toward the sophisticated, personalized, adaptive feedback systems that the authors argue elite sport actually requires.

Yet the feedback side of the equation reveals a striking monoculture. Visual feedback dominated the literature, appearing in 81 percent of studies, most commonly as on-screen graphs and numerical displays delivered via smartphone or tablet in 61 percent of the corpus. A representative system for hammer throw training gives coaches a real-time plot of the wire tension curve, elegant for a coach watching but demanding that an athlete look away from their own movement. The authors point out that visual attention during performance is a limited and critical resource, and studies on augmented reality workout systems have found that on-screen cues can impose additional cognitive load during dynamic movement. For an embodied skill like rhythm, which is often felt as much as seen, defaulting to screens may be actively counterproductive.

The untapped opportunity, the review argues, lies in the senses sport has largely ignored. Auditory feedback appeared in only 25 percent of studies and haptic feedback in a mere 11 percent. Their temporal affordances make them natural candidates for rhythm cueing: the ear is a timing organ, and the skin can feel a pulse without any attention being paid. The few studies that do explore these channels hint at real promise. Interactive sonification, in which movement is converted into continuous sound, has provided effective low-latency feedback on body roll in swimming, and electrical muscle stimulation has been used to directly correct running foot-strike patterns. Crucially, the auditory channel often remains unengaged during high-concentration physical exercise, leaving bandwidth free for guidance that refines rather than disrupts an athlete’s flow.

The review also documents a troubling gap between who builds these systems and who is supposed to benefit from them. Elite and professional athletes were the target population in only 26 percent of studies, and coaches in just 18.5 percent, while novices and amateurs, often university students, are heavily over-represented. The most common application domain was general fitness training, and 70 percent of studies targeted posture or technique form, compared with only 35 percent targeting timing or tempo, 20 percent targeting consistency or flow, and 12 percent targeting stride parameters. In other words, the literature measures discrete kinematic snapshots when rhythm is an integrated temporal quality. Elite performers possess refined, idiosyncratic motor programs; their challenge is micro-adjustment of an already optimized system, requiring higher-resolution feedback than novice learning studies typically provide.

The proof problem compounds the population problem. Although 81 percent of systems were empirically evaluated, the primary goal of most evaluations was system usability or feasibility, in 45 percent of studies, while only 22.5 percent measured performance improvement and 8.5 percent measured skill acquisition. Nearly one in five studies, 19.4 percent, reported no formal evaluation at all. Perhaps most tellingly, only 26.5 percent of studies explicitly measured rhythm or a direct synonym as an outcome variable. Systems for tennis swing classification, for example, celebrated machine learning model accuracy rather than any change in player skill. The result is a literature full of working prototypes with largely unevaluated training impact.

The authors distill these observations into four challenges, concerning what is sensed, who is studied, how feedback is delivered and what proof exists, and offer them as exploratory, hypothesis-generating directions rather than evidence-based prescriptions. Their near-term proposal is compelling in its simplicity: couple the already mature wearable inertial sensing platforms with continuous, low-latency, non-visual feedback such as movement sonification or complementary haptic cues, and design evaluations that separate technical validation, user experience and genuine performance outcomes as distinct levels of evidence, a trajectory they map onto the Technology Readiness Level framework from proof-of-concept to proof-of-impact. The review has limitations, including its restriction to English-language, technology-indexed literature across three engineering databases, so field-level conclusions about rhythm training await a cross-disciplinary synthesis. But the message to sports engineers is clear: the sensors are ready, the audio and touch channels are wide open, and the athletes at the top are still waiting for technology that speaks their temporal language.

Subject of Research: Human augmentation technologies and feedback modalities for training rhythm in elite sport

Article Title: Augmenting rhythm: a scoping review of technologies and feedback modalities in elite sport

Article References: Hossain, M. T., Lindeman, R. W., Ingram, M., & Lukosch, S. (2026). Augmenting rhythm: a scoping review of technologies and feedback modalities in elite sport. Sports Engineering, 29(2), Article 32. https://doi.org/10.1007/s12283-026-00562-7

Image Credits: AI Generated

DOI: 10.1007/s12283-026-00562-7

Keywords: rhythm, elite sport, wearable sensors, inertial measurement units, real-time feedback, sonification, haptic feedback, extended reality, computer vision, artificial intelligence, sports engineering, scoping review

The research, led by Charlotte Mabbs with Lloyd Smith, both of Washington State University, addresses a gap that has persisted in sports ball mechanics for years. While the behavior of balls in head-on, or normal, impacts is routinely measured and modeled, oblique impacts are considerably more complicated. During such an impact, a ball can either slide across the surface — if it comes in at a shallow angle or the friction between ball and surface is low — or it can grip the surface, momentarily bringing its contact patch to a halt. When the ball grips, frictional forces stretch and shear the compliant cover and core, storing elastic energy that is later released as rotation. Capturing this transition between sliding and gripping has proven a stubborn challenge for previous simulations of tennis balls, soccer balls, and golf balls.

Earlier models typically relied on a constant coefficient of friction, treating the resistance between ball and surface as a single fixed value throughout the collision. Those models could reproduce sliding behavior or gripping behavior, but not both. A tennis ball study that achieved good agreement compared its simulation to only one impact condition, leaving its general validity uncertain. Other investigations varied the friction coefficient numerically under fixed conditions without experimental validation at all. The Washington State team took a different route: they implemented what they call a temporal friction model, in which the friction coefficient evolves during contact, transitioning between independently measured static and dynamic values depending on the relative sliding velocity between ball and surface.

To build the model, the researchers first needed to characterize the softball itself. They studied adult fastpitch softballs with a circumference of 306 millimeters and a mass of 0.2 kilograms, constructed with a rigid polyurethane foam core surrounded by a thin leather cover stitched with raised seams. Upon impact, a softball dissipates roughly 75 percent of its energy, so the material model had to capture severe energy loss. Using the explicit finite element solver LS-DYNA, the team employed a non-linear viscoelastic foam material model governed by a high-speed stress-strain loading curve, with parameters controlling hysteresis and energy dissipation tuned until simulated normal impacts at 21.4 and 30.6 meters per second matched measured stiffness and coefficient of restitution within 4 percent of laboratory results.

One of the study’s most striking findings concerns the ball’s moment of inertia — a measure of how its mass is distributed around its center. A homogeneous sphere, the standard simplification in sports ball modeling, underestimated the measured moment of inertia by 9.1 percent because the dense leather cover and seams push mass toward the outside of the ball. That seemingly small discrepancy had outsized consequences: the homogeneous model overpredicted the final angular velocity of a sliding impact by about 13 percent. By adding a thin shell of massless-stiffness elements to the ball’s radius and adjusting densities to match the measured inertia, the researchers brought the angular velocity error down to just 3 percent. For balls with seams — softballs, baseballs, cricket balls — the lesson is clear: assuming a uniform sphere is not good enough when rotation is at stake.

The experimental half of the study was equally ambitious. The team fired softballs from a pneumatic cannon at a steel plate across a wide envelope of conditions: speeds from 20.1 to 63.5 meters per second, spin rates up to 117 radians per second, and impact angles from 14 to 80 degrees. A triaxial load sensor recorded normal and shear forces during contact at 150 kilohertz, while high-speed cameras filming at up to 14,100 frames per second tracked the ball’s position and rotation through the roughly 1.5-millisecond collision. Ball rotation was computed by detecting and matching distinctive features on a randomly patterned leather cover frame by frame. Between every shot, the steel plate was cleaned with 1000-grit sandpaper to keep friction conditions consistent.

Friction measurements fed directly into the model. Sliding impacts — those in which the ball skids through contact — yielded a dynamic friction coefficient of 0.360, while an inclined plane test using a panel of leather removed from an actual softball gave a static coefficient of 0.625. The dynamic value carried a relatively large uncertainty of about 22 percent, consistent with the scatter reported in prior dynamic friction measurements on other balls. The static value aligned well with published engineering data for leather against metal, which typically cites values around 0.6. The temporal friction model blended these two values with an exponential decay governed by a transition parameter, tuned to match representative sliding and gripping impacts and then validated against the full range of angles and speeds.

The validation results were emphatic. Compared with a constant friction model using the dynamic coefficient, the temporal friction model reduced the mean normalized root-mean-square error in predicted angular velocity during contact by 29 percent, and by 81 percent compared with a constant friction model based on the static coefficient. Crucially, it was the first friction formulation for any sports ball to describe both sliding and gripping behavior simultaneously. In gripping impacts, the simulated friction coefficient lingered near the dynamic value for only about 10 percent of the contact duration before climbing rapidly to the static value as the ball’s contact patch came to rest; in sliding impacts, the coefficient stayed near the dynamic value for nearly half the impact. Predicted peak normal forces came within 2.5 percent of experiment, and tangential forces within 6.1 percent.

The model also reproduced the distinctive energy landscape of oblique impacts. As impact angle decreases from vertical, more of the ball’s incoming kinetic energy is converted into transverse motion and rotation, with rotational energy peaking at the shallowest angles at which the ball still grips the surface. The simulation correctly captured the inflection point — between 25 and 30 degrees — below which the ball slides through contact rather than gripping. Interestingly, the frictional force did not substantially reverse during contact, unlike the dramatic reversals seen in highly elastic superballs, a difference the researchers attribute to the softball’s prodigious energy dissipation. One residual discrepancy remained: the simulated frictional force peaked slightly earlier than measured, by roughly 0.1 to 0.16 milliseconds. Tests on a coverless ball, with the leather stripped away, largely eliminated the timing gap, suggesting the thin cover — only 10 percent of the ball’s volume — measurably influences shear response, perhaps through slip at the core-cover interface or the cover’s own compliance.

The implications extend beyond softball. Because softballs are simple in construction compared with the layered pills, yarn windings, and seams of baseballs and cricket balls, the inhomogeneity effects documented here are likely even more pronounced in those sports. The work also marks the first dynamic measurement of friction coefficients for a solid sports ball at speeds representative of actual play, and the first controlled laboratory experiments on softball oblique impacts of any kind — previous on-field studies of softball-bat collisions had reported lower tangential restitution values, consistent with the greater energy dissipation expected when a compliant, curved bat is involved. For governing bodies, equipment designers, and modelers of ball flight, the message is that both the velocity-dependent nature of friction and the true mass distribution of the ball must be respected. As the authors conclude, ball inhomogeneity and temporal friction are not refinements but necessities for accurately modeling how solid sports balls shear, grip, and spin when they meet the ground.

Subject of Research: Finite element modeling and experimental validation of oblique, frictional impacts of softballs

Article Title: Finite element modeling of oblique impacts of softballs

Article References: Mabbs, C., & Smith, L. (2026). Finite element modeling of oblique impacts of softballs. Sports Engineering, 29(2), Article 31. https://doi.org/10.1007/s12283-026-00564-5

Image Credits: AI Generated

DOI: 10.1007/s12283-026-00564-5

Keywords: softball, finite element model, oblique impact, friction, spin, sports engineering, coefficient of restitution, moment of inertia, LS-DYNA, sliding and gripping, shear deformation, sports biomechanics