Greater foot and footwear mechanical work associated with less ankle joint work during running.

Footwear energy storage and return is often suggested as one explanation for metabolic energy savings when running in Advanced Athletic Footwear. However, there is no common understanding of how footwear energy storage and return facilitates changes in muscle and joint kinetics. The purpose of this study was to evaluate the magnitude and timing of foot, footwear and lower limb joint powers and work while running in Advanced and Traditional Athletic Footwear. Fifteen runners participated in an overground motion analysis study. Since footwear kinetics are methodologically challenging to quantify, we leveraged distal rearfoot power analyses ('foot + footwear' power) and evaluated changes in the magnitude and timing of foot + footwear power and lower limb joint powers. Running in Advanced Footwear resulted in greater foot + footwear work, compared to Traditional Shoes, and lower positive ankle work, potentially reducing the muscular demand on the runner. The timing of foot + footwear power varied only slightly across footwear. There are exciting innovation opportunities to manipulate the timing of footwear energy and return. This study demonstrates the research value of quantifying time-series foot + footwear power, and points industry developers towards footwear innovation opportunities.

A foot and footwear mechanical power theoretical framework: Towards understanding energy storage and return in running footwear.

There is extreme interest surrounding the influence of advanced running footwear on running performance. The magnitude, timing, and location of mechanical energy storage and return in footwear may elucidate one way footwear influences running performance. However, the complexity of footwear makes it challenging to model footwear energy storage and return during running. The purpose of this study was to develop a practical framework for evaluating foot and footwear mechanical power profiles during running. First, a unified deformable power analysis (distal rearfoot power) was used to quantify mechanical power of the foot + footwear system. Then, qualitative mechanical power profiles of individual foot and footwear structure were developed using prior literature, benchtop footwear material properties, and experimental kinetics and kinematics. The result is a framework for understanding foot and footwear mechanical power during running using a two-stage analysis. First, foot + footwear power can be experimentally compared when running in various footwear constructions. Second, the developed framework can provide qualitative insights into which foot and footwear structures may contribute to differences in measured foot + footwear power. To highlight the utility of this framework, the timing, magnitude, and location of foot + footwear power is compared when running in different footwear constructions and with different running styles. The framework developed here provides a practical tool for footwear developers and researchers to gain intuition about the timing, relative magnitude, and location of energy storage and return from footwear during running. There are opportunities to expand on this framework to further connect footwear construction to running performance.

A Promising Wearable Solution for the Practical and Accurate Monitoring of Low Back Loading in Manual Material Handling.

(1) Background: Low back disorders are a leading cause of missed work and physical disability in manual material handling due to repetitive lumbar loading and overexertion. Ergonomic assessments are often performed to understand and mitigate the risk of musculoskeletal overexertion injuries. Wearable sensor solutions for monitoring low back loading have the potential to improve the quality, quantity, and efficiency of ergonomic assessments and to expand opportunities for the personalized, continuous monitoring of overexertion injury risk. However, existing wearable solutions using a single inertial measurement unit (IMU) are limited in how accurately they can estimate back loading when objects of varying mass are handled, and alternative solutions in the scientific literature require so many distributed sensors that they are impractical for widespread workplace implementation. We therefore explored new ways to accurately monitor low back loading using a small number of wearable sensors. (2) Methods: We synchronously collected data from laboratory instrumentation and wearable sensors to analyze 10 individuals each performing about 400 different material handling tasks. We explored dozens of candidate solutions that used IMUs on various body locations and/or pressure insoles. (3) Results: We found that the two key sensors for accurately monitoring low back loading are a trunk IMU and pressure insoles. Using signals from these two sensors together with a Gradient Boosted Decision Tree algorithm has the potential to provide a practical (relatively few sensors), accurate (up to r2 = 0.89), and automated way (using wearables) to monitor time series lumbar moments across a broad range of material handling tasks. The trunk IMU could be replaced by thigh IMUs, or a pelvis IMU, without sacrificing much accuracy, but there was no practical substitute for the pressure insoles. The key to realizing accurate lumbar load estimates with this approach in the real world will be optimizing force estimates from pressure insoles. (4) Conclusions: Here, we present a promising wearable solution for the practical, automated, and accurate monitoring of low back loading during manual material handling.

Combining wearable sensor signals, machine learning and biomechanics to estimate tibial bone force and damage during running.

There are tremendous opportunities to advance science, clinical care, sports performance, and societal health if we are able to develop tools for monitoring musculoskeletal loading (e.g., forces on bones or muscles) outside the lab. While wearable sensors enable non-invasive monitoring of human movement in applied situations, current commercial wearables do not estimate tissue-level loading on structures inside the body. Here we explore the feasibility of using wearable sensors to estimate tibial bone force during running. First, we used lab-based data and musculoskeletal modeling to estimate tibial force for ten participants running across a range of speeds and slopes. Next, we converted lab-based data to signals feasibly measured with wearables (inertial measurement units on the foot and shank, and pressure-sensing insoles) and used these data to develop two multi-sensor algorithms for estimating peak tibial force: one physics-based and one machine learning. Additionally, to reflect current running wearables that utilize running impact metrics to infer musculoskeletal loading or injury risk, we estimated tibial force using a commonly measured impact metric, the ground reaction force vertical average loading rate (VALR). Using VALR to estimate peak tibial force resulted in a mean absolute percent error of 9.9%, which was no more accurate than a theoretical step counter that assumed the same peak force for every running stride. Our physics-based algorithm reduced error to 5.2%, and our machine learning algorithm reduced error to 2.6%. Further, to gain insights into how force estimation accuracy relates to overuse injury risk, we computed bone damage expected due to a given loading cycle. We found that modest errors in tibial force translated into large errors in bone damage estimates. For example, a 9.9% error in tibial force using VALR translated into 104% error in estimated bone damage. Encouragingly, the physics-based and machine learning algorithms reduced damage errors to 41% and 18%, respectively. This study highlights the exciting potential to combine wearables, musculoskeletal biomechanics and machine learning to develop more accurate tools for monitoring musculoskeletal loading in applied situations.

Ground reaction force metrics are not strongly correlated with tibial bone load when running across speeds and slopes: Implications for science, sport and wearable tech.

INTRODUCTION: Tibial stress fractures are a common overuse injury resulting from the accumulation of bone microdamage due to repeated loading. Researchers and wearable device developers have sought to understand or predict stress fracture risks, and other injury risks, by monitoring the ground reaction force (GRF, the force between the foot and ground), or GRF correlates (e.g., tibial shock) captured via wearable sensors. Increases in GRF metrics are typically assumed to reflect increases in loading on internal biological structures (e.g., bones). The purpose of this study was to evaluate this assumption for running by testing if increases in GRF metrics were strongly correlated with increases in tibial compression force over a range of speeds and slopes. METHODS: Ten healthy individuals performed running trials while we collected GRFs and kinematics. We assessed if commonly-used vertical GRF metrics (impact peak, loading rate, active peak, impulse) were strongly correlated with tibial load metrics (peak force, impulse). RESULTS: On average, increases in GRF metrics were not strongly correlated with increases in tibial load metrics. For instance, correlating GRF impact peak and loading rate with peak tibial load resulted in r = -0.29±0.37 and r = -0.20±0.35 (inter-subject mean and standard deviation), respectively. We observed high inter-subject variability in correlations, though most coefficients were negligible, weak or moderate. Seventy-six of the 80 subject-specific correlation coefficients computed indicated that higher GRF metrics were not strongly correlated with higher tibial forces. CONCLUSIONS: These results demonstrate that commonly-used GRF metrics can mislead our understanding of loading on internal structures, such as the tibia. Increases in GRF metrics should not be assumed to be an indicator of increases in tibial bone load or overuse injury risk during running. This has important implications for sports, wearable devices, and research on running-related injuries, affecting >50 scientific publications per year from 2015-2017.

Ultrasound estimates of Achilles tendon exhibit unexpected shortening during ankle plantarflexion.

The purpose of this study was to investigate Achilles tendon (AT) length changes during a series of tasks that involved combinations of higher/lower force, and larger/smaller length changes of the medial gastrocnemius muscle-tendon unit (MTU). We sought to determine if common ultrasound-based estimates of AT length change were consistent with expectations for a passive elastic tendon acting in series with a muscle. We tested 8 healthy individuals during restricted joint calf contractions (high force, low displacement), ankle dorsi-/plantar-flexion (DF/PF) with the foot in the air (low force, high displacement), and heel raises (high force, high displacement). We experimentally estimated AT length change using two ultrasound methods, one based on muscle-tendon junction (MTJ) tracking and one based on muscle fascicle (MF) tracking. Estimates of AT length change were consistent with model expectations during restricted calf contractions, when the MTU underwent minimal length change. However, estimates of AT length changes were inconsistent with model expectations during the ankle DF/PF and heel raise tasks. Specifically, the AT was estimated to shorten substantially, often 10-20 mm, when the ankle plantarflexed beyond neutral position, despite loading conditions in which a passive, stiff spring would be expected to either lengthen (under increasing force) or maintain its length (under low force). These unexpected findings suggest the need for improvements in how we conceptually model and/or experimentally estimate MTU dynamics in vivo during motion analysis studies, particularly when the ankle plantarflexes beyond neutral.