It’s positive to be negative: Achilles tendon work loops during human locomotion

Ultrasound imaging is increasingly used with motion and force data to quantify tendon dynamics during human movement. Frequently, tendon dynamics are estimated indirectly from muscle fascicle kinematics (by subtracting muscle from muscle-tendon unit length), but there is mounting evidence that this Indirect approach yields implausible tendon work loops. Since tendons are passive viscoelastic structures, when they undergo a loading-unloading cycle they must exhibit a negative work loop (i.e., perform net negative work). However, prior studies using this Indirect approach report large positive work loops, often estimating that tendons return 2–5 J of elastic energy for every 1 J of energy stored. More direct ultrasound estimates of tendon kinematics have emerged that quantify tendon elongations by tracking either the muscle-tendon junction or localized tendon tissue. However, it is unclear if these yield more plausible estimates of tendon dynamics. Our objective was to compute tendon work loops and hysteresis losses using these two Direct tendon kinematics estimates during human walking. We found that Direct estimates generally resulted in negative work loops, with average tendon hysteresis losses of 2–11% at 1.25 m/s and 33–49% at 0.75 m/s (N = 8), alluding to 0.51–0.98 J of tendon energy returned for every 1 J stored. We interpret this finding to suggest that Direct approaches provide more plausible estimates than the Indirect approach, and may be preferable for understanding tendon energy storage and return. However, the Direct approaches did exhibit speed-dependent trends that are not consistent with isolated, in vitro tendon hysteresis losses of about 5–10%. These trends suggest that Direct estimates also contain some level of error, albeit much smaller than Indirect estimates. Overall, this study serves to highlight the complexity and difficulty of estimating tendon dynamics non-invasively, and the care that must be taken to interpret biological function from current ultrasound-based estimates

Six degree-of-freedom analysis of hip, knee, ankle and foot provides updated understanding of biomechanical work during human walking

Measuring biomechanical work performed by humans and other animals is critical for understanding muscle–tendon function, jointspecific contributions and energy-saving mechanisms during locomotion. Inverse dynamics is often employed to estimate jointlevel contributions, and deformable body estimates can be used to study work performed by the foot. We recently discovered that these commonly used experimental estimates fail to explain whole-body energy changes observed during human walking. By re-analyzing previously published data, we found that about 25% (8 J) of total positive energy changes of/about the body’s center-of-mass and \u3e30% of the energy changes during the Push-off phase of walking were not explained by conventional joint- and segment-level work estimates, exposing a gap in our fundamental understanding of work production during gait. Here, we present a novel Energy-Accounting analysis that integrates various empirical measures of work and energy to elucidate the source of unexplained biomechanical work. We discovered that by extending conventional 3 degree-of-freedom (DOF) inverse dynamics (estimating rotational work about joints) to 6DOF (rotational and translational) analysis of the hip, knee, ankle and foot, we could fully explain the missing positive work. This revealed that Push-off work performed about the hip may be \u3e50% greater than conventionally estimated (9.3 versus 6.0 J, P=0.0002, at 1.4 m s−1 ). Our findings demonstrate that 6DOF analysis (of hip– knee–ankle–foot) better captures energy changes of the body than more conventional 3DOF estimates. These findings refine our fundamental understanding of how work is distributed within the body, which has implications for assistive technology, biomechanical simulations and potentially clinical treatment

Mechanical misconceptions: Have we lost the "mechanics" in "sports biomechanics"?

Biomechanics principally stems from two disciplines, mechanics and biology. However, both the application and language of the mechanical constructs are not always adhered to when applied to biological systems, which can lead to errors and misunderstandings within the scientific literature. Here we address three topics that seem to be common points of confusion and misconception, with a specific focus on sports biomechanics applications: (1) joint reaction forces as they pertain to loads actually experienced by biological joints; (2) the partitioning of scalar quantities into directional components; and (3) weight and gravity alteration. For each topic, we discuss how mechanical concepts have been commonly misapplied in peer-reviewed publications, the consequences of those misapplications, and how biomechanics, exercise science, and other related disciplines can collectively benefit by more carefully adhering to and applying concepts of classical mechanics

Preventing emerging infectious diseases : a strategy for the 21st century : overview of the updated CDC plan

Societal, technological, and environmental factors continue to have a dramatic effect on infectious diseases worldwide, facilitating the emergence of new diseases and the reemergence of old ones, sometimes in drug-resistant forms. Modern demographic and ecologic conditions that favor the spread of infectious diseases include rapid population growth; increasing poverty and urban migration; more frequent movement across international boundaries by tourists, workers, immigrants, and refugees; alterations in the habitats of animals and arthropods that transmit disease; increasing numbers of persons with impaired host defenses; and changes in the way that food is processed and distributed. Several recent health events underscore the need for a public health system ready to address whatever disease problems that might arise. For example, in 1997, an avian strain of influenza that had never before infected humans began to kill previously healthy persons in Hong Kong, and strains of Sta phylococcus aureus with diminished susceptibility to the antibiotic vancomycin were reported in Japan and the United States. In addition, researchers recently discovered that a strain of the virus that causes acquired immunodeficiency syndrome (AIDS) had been infecting humans for at least 20 years before AIDS emerged as a worldwide epidemic. Preventing Emerging Infectious Diseases: A Strategy for the 21st Century describes CDC's plan to combat today's infectious diseases and prevent those of tomorrow. It represents the second phase of the effort launched in 1994 with the publication of CDC's Addressing Emerging Infectious Disease Threats: A Prevention Strategy for the United States. This overview of the updated plan outlines specific objectives under four major goals: a) surveillance and response, b) applied research, c) infrastructure and training, and d) prevention and control. Achieving these objectives will enhance understanding of infectious diseases and bolster their detection, control, and prevention. The plan also targets nine categories of problems that cause human suffering and place a burden on society. The aim of this plan is to build a stronger, more flexible U.S. public health system that is well-prepared to respond to known disease problems, as well as to address the unexpected, whether it be an influenza pandemic, a disease caused by an unknown organism, or a bioterrorist attack. The implementation of this plan will require the dedicated efforts of many partners, including state and local health departments, other federal agencies, professional societies, universities, research institutes, health-care providers and organizations, the World Health Organization, and many other domestic and international organizations and groups.September 11, 1998.The following CDC staff members prepared this report: Suzanne Binder, Alexandra M. Levitt, National Center for Infectious Diseases, and National Center for Infectious Diseases Plan Steering Committee.Includes bibliographical references.199

Mechanical Work as an Indirect Measure of Subjective Costs Influencing Human Movement

To descend a flight of stairs, would you rather walk or fall? Falling seems to have some obvious disadvantages such as the risk of pain or injury. But the preferred strategy of walking also entails a cost for the use of active muscles to perform negative work. The amount and distribution of work a person chooses to perform may, therefore, reflect a subjective valuation of the trade-offs between active muscle effort and other costs, such as pain. Here we use a simple jump landing experiment to quantify the work humans prefer to perform to dissipate the energy of landing. We found that healthy normal subjects (N = 8) preferred a strategy that involved performing 37% more negative work than minimally necessary (P<0.001) across a range of landing heights. This then required additional positive work to return to standing rest posture, highlighting the cost of this preference. Subjects were also able to modulate the amount of landing work, and its distribution between active and passive tissues. When instructed to land softly, they performed 76% more work than necessary (P<0.001), with a higher proportion from active muscles (89% vs. 84%, P<0.001). Stiff-legged landings, performed by one subject for demonstration, exhibited close to the minimum of work, with more of it performed passively through soft tissue deformations (at least 30% in stiff landings vs. 16% preferred). During jump landings, humans appear not to minimize muscle work, but instead choose to perform a consistent amount of extra work, presumably to avoid other subjective costs. The degree to which work is not minimized may indirectly quantify the relative valuation of costs that are otherwise difficult to measure

It's positive to be negative: Achilles tendon work loops during human locomotion.

Ultrasound imaging is increasingly used with motion and force data to quantify tendon dynamics during human movement. Frequently, tendon dynamics are estimated indirectly from muscle fascicle kinematics (by subtracting muscle from muscle-tendon unit length), but there is mounting evidence that this Indirect approach yields implausible tendon work loops. Since tendons are passive viscoelastic structures, when they undergo a loading-unloading cycle they must exhibit a negative work loop (i.e., perform net negative work). However, prior studies using this Indirect approach report large positive work loops, often estimating that tendons return 2-5 J of elastic energy for every 1 J of energy stored. More direct ultrasound estimates of tendon kinematics have emerged that quantify tendon elongations by tracking either the muscle-tendon junction or localized tendon tissue. However, it is unclear if these yield more plausible estimates of tendon dynamics. Our objective was to compute tendon work loops and hysteresis losses using these two Direct tendon kinematics estimates during human walking. We found that Direct estimates generally resulted in negative work loops, with average tendon hysteresis losses of 2-11% at 1.25 m/s and 33-49% at 0.75 m/s (N = 8), alluding to 0.51-0.98 J of tendon energy returned for every 1 J stored. We interpret this finding to suggest that Direct approaches provide more plausible estimates than the Indirect approach, and may be preferable for understanding tendon energy storage and return. However, the Direct approaches did exhibit speed-dependent trends that are not consistent with isolated, in vitro tendon hysteresis losses of about 5-10%. These trends suggest that Direct estimates also contain some level of error, albeit much smaller than Indirect estimates. Overall, this study serves to highlight the complexity and difficulty of estimating tendon dynamics non-invasively, and the care that must be taken to interpret biological function from current ultrasound-based estimates

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

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

Direct MTJ estimation method and tendon work loops during walking.

<p>Left: The Direct MTJ method estimates tendon length changes by summing the instantaneous position of the ultrasound transducer with respect to the tendon’s insertion, <i>L</i>₁, and the displacement of the MTJ within the transducer’s field of view, <i>L</i>_2,<i>MTJ</i>. In this method the ultrasound transducer is positioned over the MTJ. Right: This Direct MTJ method, applied to the LG MTU, yields negative work loops on average, for all walking speeds tested. Hysteresis losses were estimated to decrease with increasing speed, from an average of 49% at 0.75 m/s to 11% at 1.25m/s. Average (inter-subject) mean results are depicted (<i>N</i> = 8).</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