Despite frequently declared benefits of using wireless accelerometers to assess running gait in real-world settings, available research is limited. The purpose of this study was to investigate outdoor surface effects on dynamic stability and dynamic loading during running using tri-axial trunk accelerometry. Twenty eight runners (11 highly-trained, 17 recreational) performed outdoor running on three outdoor training surfaces (concrete road, synthetic track and woodchip trail) at self-selected comfortable running speeds. Dynamic postural stability (tri-axial acceleration root mean square (RMS) ratio, step and stride regularity, sample entropy), dynamic loading (impact and breaking peak amplitudes and median frequencies), as well as spatio-temporal running gait measures (step frequency, stance time) were derived from trunk accelerations sampled at 1024Hz. Results from generalized estimating equations (GEE) analysis showed that compared to concrete road, woodchip trail had several significant effects on dynamic stability (higher AP ratio of acceleration RMS, lower ML inter-step and inter-stride regularity), on dynamic loading (downward shift in vertical and AP median frequency), and reduced step frequency (p<0.05). Surface effects were unaffected when both running level and running speed were added as potential confounders. Results suggest that woodchip trails disrupt aspects of dynamic stability and loading that are detectable using a single trunk accelerometer. These results provide further insight into how runners adapt their locomotor biomechanics on outdoor surfaces in situ.

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Research has focused on parameters that are associated with injury risk, e.g. vertical acceleration. These parameters can be influenced by running on different surfaces or at different running speeds, but the relationship between them is not completely clear. Understanding the relationship may result in training guidelines to reduce the injury risk. In this study, thirty-five participants with three different levels of running experience were recruited. Participants ran on three different surfaces (concrete, synthetic running track, and woodchip trail) at two different running speeds: a self-selected comfortable speed and a fixed speed of 3.06 m/s. Vertical acceleration of the lower leg was measured with an accelerometer. The vertical acceleration was significantly lower during running on the woodchip trail in comparison with the synthetic running track and the concrete, and significantly lower during running at lower speed in comparison with during running at higher speed on all surfaces. No significant differences in vertical acceleration were found between the three groups of runners at fixed speed. Higher self-selected speed due to higher performance level also did not result in higher vertical acceleration. These results may show that running on a woodchip trail and slowing down could reduce the injury risk at the tibia.

The interaction between gastrocnemius medialis (GM) muscle and Achilles tendon, i.e., muscle-tendon unit (MTU) interaction, plays an important role in minimizing the metabolic cost of running. Foot-strike pattern (FSP) has been suggested to alter MTU interaction and subsequently the metabolic cost of running. However, metabolic data from experimental studies on FSP are inconsistent, and a comparison of MTU interaction between FSP is still lacking. We, therefore, investigated the effect of habitual rearfoot and mid-/forefoot striking on MTU interaction, ankle joint work, and plantar flexor muscle force production while running at 10 and 14 km/h. GM muscle fascicles of 9 rearfoot and 10 mid-/forefoot strikers were tracked using dynamic ultrasonography during treadmill running. We collected kinetic and kinematic data and used musculoskeletal models to determine joint angles and calculate MTU lengths. In addition, we used dynamic optimization to assess plantar flexor muscle forces. During ground contact, GM fascicle shortening ( P = 0.02) and average contraction velocity ( P = 0.01) were 40–45% greater in rearfoot strikers than mid-/forefoot strikers. Differences in contraction velocity were especially prominent during early ground contact. Moreover, GM ( P = 0.02) muscle force was greater during early ground contact in mid-/forefoot strikers than rearfoot strikers. Interestingly, we did not find differences in stretch or recoil of the series elastic element between FSP. Our results suggest that, for the GM, the reduced muscle energy cost associated with lower fascicle contraction velocity in mid-/forefoot strikers may be counteracted by greater muscle forces during early ground contact. NEW & NOTEWORTHY Kinetic and kinematic differences between foot-strike patterns during running imply (not previously reported) altered muscle-tendon interaction. Here, we studied muscle-tendon interaction using ultrasonography. We found greater fascicle contraction velocities and lower muscle forces in rearfoot compared with mid-/forefoot strikers. Our results suggest that the higher metabolic energy demand due to greater fascicle contraction velocities might offset the lower metabolic energy demand due to lower muscle forces in rearfoot compared with mid-/forefoot strikers.

There is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures, however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear support vector machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision-making by the machine learning classification model, a layer-wise relevance propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance scores for each individual input value. In other words, it provided information regarding which features of the time-varying EMG profiles were unique to each individual. Through extensive testing, we have shown that the LRP results, and by extent the activation signatures, are highly consistent between conditions and across days. In addition, they are minimally influenced by the dataset used to train the model. Additionally, we proposed a method for visualizing each individual's muscle activation signature, which has several potential clinical and scientific applications. This is the first study to provide conclusive evidence of the existence of individual muscle activation signatures.