Changes in ankle work, foot work, and tibialis anterior activation throughout a long run

Background The ankle and foot together contribute to over half of the positive and negative work performed by the lower limbs during running. Yet, little is known about how foot kinetics change throughout a run. The amount of negative foot work may decrease as tibialis anterior (TA) electromyography (EMG) changes throughout longer-duration runs. Therefore, we examined ankle and foot work as well as TA EMG changes throughout a changing-speed run. Methods Fourteen heel-striking subjects ran on a treadmill for 58 min. We collected ground reaction forces, motion capture, and EMG. Subjects ran at 110%, 100%, and 90% of their 10-km running speed and 2.8 m/s multiple times throughout the run. Foot work was evaluated using the distal rearfoot work, which provides a net estimate of all work contributors within the foot. Results Positive foot work increased and positive ankle work decreased throughout the run at all speeds. At the 110% 10-km running speed, negative foot work decreased and TA EMG frequency shifted lower throughout the run. The increase in positive foot work may be attributed to increased foot joint work performed by intrinsic foot muscles. Changes in negative foot work and TA EMG frequency may indicate that the TA plays a role in negative foot work in the early stance of a run. Conclusion This study is the first to examine how the kinetic contributions of the foot change throughout a run. Future studies should investigate how increases in foot work affect running performance

Evaluating footwear “in the wild”: Examining wrap and lace trail shoe closures during trail running

Trail running participation has grown over the last two decades. As a result, there have been an increasing number of studies examining the sport. Despite these increases, there is a lack of understanding regarding the effects of footwear on trail running biomechanics in ecologically valid conditions. The purpose of our study was to evaluate how a Wrap vs. Lace closure (on the same shoe) impacts running biomechanics on a trail. Thirty subjects ran a trail loop in each shoe while wearing a global positioning system (GPS) watch, heart rate monitor, inertial measurement units (IMUs), and plantar pressure insoles. The Wrap closure reduced peak foot eversion velocity (measured via IMU), which has been associated with fit. The Wrap closure also increased heel contact area, which is also associated with fit. This increase may be associated with the subjective preference for the Wrap. Lastly, runners had a small but significant increase in running speed in the Wrap shoe with no differences in heart rate nor subjective exertion. In total, the Wrap closure fit better than the Lace closure on a variety of terrain. This study demonstrates the feasibility of detecting meaningful biomechanical differences between footwear features in the wild using statistical tools and study design. Evaluating footwear in ecologically valid environments often creates additional variance in the data. This variance should not be treated as noise; instead, it is critical to capture this additional variance and challenges of ecologically valid terrain if we hope to use biomechanics to impact the development of new products

Inferring Muscle-Tendon Unit Power from Ankle Joint Power during the Push-Off Phase of Human Walking: Insights from a Multiarticular EMG-Driven Model

<div><p>Introduction</p><p>Inverse dynamics joint kinetics are often used to infer contributions from underlying groups of muscle-tendon units (MTUs). However, such interpretations are confounded by multiarticular (multi-joint) musculature, which can cause inverse dynamics to over- or under-estimate net MTU power. Misestimation of MTU power could lead to incorrect scientific conclusions, or to empirical estimates that misguide musculoskeletal simulations, assistive device designs, or clinical interventions. The objective of this study was to investigate the degree to which ankle joint power overestimates net plantarflexor MTU power during the Push-off phase of walking, due to the behavior of the flexor digitorum and hallucis longus (FDHL)–multiarticular MTUs crossing the ankle and metatarsophalangeal (toe) joints.</p><p>Methods</p><p>We performed a gait analysis study on six healthy participants, recording ground reaction forces, kinematics, and electromyography (EMG). Empirical data were input into an EMG-driven musculoskeletal model to estimate ankle power. This model enabled us to parse contributions from mono- and multi-articular MTUs, and required only one scaling and one time delay factor for each subject and speed, which were solved for based on empirical data. Net plantarflexing MTU power was computed by the model and quantitatively compared to inverse dynamics ankle power.</p><p>Results</p><p>The EMG-driven model was able to reproduce inverse dynamics ankle power across a range of gait speeds (R² ≥ 0.97), while also providing MTU-specific power estimates. We found that FDHL dynamics caused ankle power to slightly overestimate net plantarflexor MTU power, but only by ~2–7%.</p><p>Conclusions</p><p>During Push-off, FDHL MTU dynamics do not substantially confound the inference of net plantarflexor MTU power from inverse dynamics ankle power. However, other methodological limitations may cause inverse dynamics to overestimate net MTU power; for instance, due to rigid-body foot assumptions. Moving forward, the EMG-driven modeling approach presented could be applied to understand other tasks or larger multiarticular MTUs.</p></div

Electromechanical delay (EMD, <i>τ</i>) and scaling factor, <i>C</i>, for each speed, subject, and the overall study average.

<p>Electromechanical delay (EMD, <i>τ</i>) and scaling factor, <i>C</i>, for each speed, subject, and the overall study average.</p

Simplified representation of ankle-foot musculoskeletal model.

<p>This simplified model was used to investigate the ankle plantarflexor muscles during the Push-off phase of walking. (A) The main ankle plantarflexor MTUs were included in the model: triceps surae (soleus and gastrocnemius), the peroneus longus, and the flexor digitorum and hallucis longus (FDHL). See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163169#pone.0163169.s001" target="_blank">S1 Appendix</a> for more details on muscles that were included/excluded. (B) Kinematic, anthropomorphic, and EMG data were used to estimate power contributions from each MTU. An example is depicted for the multiarticular FDHL MTUs. Anthropomorphic MTU moment arms about the ankle (<i>r</i>_{<i>fdhl</i>,<i>ank</i>}) and MTP joints (<i>r</i>_{<i>fdhl</i>,<i>mtp</i>}) were combined with kinematic estimates–angular velocities of the ankle (<i>ω</i>_<i>ank</i>) and MTP joints (<i>ω</i>_<i>mtp</i>), and longitudinal arch length (<i>l</i>_<i>arch</i>)–to estimate time-varying MTU length changes. MTU force was estimated using an EMG-to-force mapping algorithm (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163169#pone.0163169.s001" target="_blank">S1 Appendix</a> for full details). Force was then multiplied by the rate of MTU length change to compute MTU power.</p

Net ankle power vs. minimum MTU power vs. MTU power during human walking at 1.25 m/s (N = 6).

<p>Net ankle power overestimated MTU power and minimum MTU power by about 2% and 7%, respectively, due to multiarticular FDHL dynamics. Inset: Push-off work (area under the power curve within the shaded region) exhibited similar, relatively small differences.</p

Average MTU contributions to at 1.25 m/s.

<p>Estimated MTU contributions are shown for the triceps surae, flexor digitorum and hallucis longus (FDHL), and peroneus longus (<i>N</i> = 6).</p

Conceptual summary of ankle joint vs. muscle-tendon-unit (MTU) power.

<p>Net ankle joint power (green, top row) can be computed from inverse dynamics by multiplying ankle joint moment, <i>M</i>_<i>ank</i>, by ankle angular velocity, <i>ω</i>_<i>ank</i> (sagittal plane depicted). Due to assumptions in inverse dynamics, this ankle power may or may not correspond with net MTU power (second row), depending on the underlying MTU contributions (cartoon depicted in bottom row). (A) Ankle power is expected to reflect MTU power when MTUs are monoarticular (red, acting solely about the ankle). (B) Ankle power may not reflect power contributions from multiarticular MTUs (blue). In the extreme example depicted, the multiarticular MTU provides torque about both the ankle and MTP (toe) joints, but due to the simultaneous plantarflexion of the ankle and extension of the toes, the MTU does not change length. Thus the MTU behaves like a rigid cable and performs zero net power. However, inverse dynamics (joint-by-joint) analysis would indicate positive power about the ankle (<i>M</i>_<i>ank</i> ∙ <i>ω</i>_<i>ank</i>), and equal offsetting negative power about the toe joints (<i>M</i>_<i>MTP</i> ∙ <i>ω</i>_<i>mtp</i>, inset). In this case, net ankle power would greatly overestimate net MTU power. (C) In actuality, both mono- and multi-articular MTUs contribute to human movement. However, it remains unclear if and by how much ankle power overestimates net MTU power. If multiarticular MTUs act isometrically (i.e., perform zero net power, as depicted here) or close to isometrically, then it is expected that ankle power magnitude will be larger than net MTU power.</p

Shoe feature recommendations for different running levels: A Delphi study.

Providing runners with footwear that match their functional needs has the potential to improve footwear comfort, enhance running performance and reduce the risk of overuse injuries. It is currently not known how footwear experts make decisions about different shoe features and their properties for runners of different levels. We performed a Delphi study in order to understand: 1) definitions of different runner levels, 2) which footwear features are considered important and 3) how these features should be prescribed for runners of different levels. Experienced academics, journalists, coaches, bloggers and physicians that examine the effects of footwear on running were recruited to participate in three rounds of a Delphi study. Three runner level definitions were refined throughout this study based on expert feedback. Experts were also provided a list of 20 different footwear features. They were asked which features were important and what the properties of those features should be. Twenty-four experts, most with 10+ years of experience, completed all three rounds of this study. These experts came to a consensus for the characteristics of three different running levels. They indicated that 12 of the 20 footwear features initially proposed were important for footwear design. Of these 12 features, experts came to a consensus on how to apply five footwear feature properties for all three different running levels. These features were: upper breathability, forefoot bending stiffness, heel-to-toe drop, torsional bending stiffness and crash pad. Interestingly, the experts were not able to come to a consensus on one of the most researched footwear features, rearfoot midsole hardness. These recommendations can provide a starting point for further biomechanical studies, especially for features that are considered as important, but have not yet been examined experimentally

Estimating Running Ground Reaction Forces from Plantar Pressure during Graded Running

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