Estimating Upper Extremity Joint Contributions in Functional Motions to Create a Metric for Injury Prevention Using OpenSim

Functional movements have the potential to predict risk of injury based on the frequency of these movements on a daily basis. In order for a metric to be established, a variety of healthy subjects must be recorded performing these motions. It was predicted that motions covering a large three-dimensional workspace will require greater contributions from the shoulder joint, while motions with more of a planar workspace will require greater contributions from the elbow joint. Ten subjects, five male and five female, performed two different functional tasks involving the upper extremity. Pointing and drinking tasks were selected based on the frequency that these tasks are performed on a daily basis. Each subject was affixed with passive reflective markers according to a previously developed OpenSim upper extremity model. The subject performed each task eight times while being recording with a Vicon camera system. The five trials with the least amount of marker exclusion were chosen for analysis. The inverse kinematics and dynamics for each trial were determined using OpenSim and then filtered through a low pass filter. The resulting ranges of motion for shoulder elevation and elbow flexion for both the drinking and pointing tasks were as expected. For the drinking task, the majority of subjects flexed their elbow 60° and their shoulder elevation fluctuated between 30-50°. For the pointing task, the majority of the subjects elbow flexion varied no more than 10°, and their shoulder elevation fluctuated between 70-95°. Figure 1 shows the results of one of the subjects. From this preliminary study, it is observed that most of the subjects use similar kinematics to accomplish each task; however, several of the subjects displayed irregular kinematics during some of their trials. Overall, these results are consistent with previous studies [1]. This preliminary study provides a foundation for future studies focusing on injury prevention and performance enhancement for athletes. A range of functional movements will be recorded and analyzed to develop quantitative metrics that will serve as indicators for potential injuries and will also be used to inform training protocols aimed at reducing the overall risk of injuries for athletes

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

Estimating Upper Extremity Joint Contributions in Functional Motions to Create a Metric for Injury Prevention Using OpenSim

Functional movements have the potential to predict risk of injury based on the frequency of these movements on a daily basis. In order for a metric to be established, a variety of healthy subjects must be recorded performing these motions. It was predicted that motions covering a large three-dimensional workspace will require greater contributions from the shoulder joint, while motions with more of a planar workspace will require greater contributions from the elbow joint. Ten subjects, five male and five female, performed two different functional tasks involving the upper extremity. Pointing and drinking tasks were selected based on the frequency that these tasks are performed on a daily basis. Each subject was affixed with passive reflective markers according to a previously developed OpenSim upper extremity model. The subject performed each task eight times while being recording with a Vicon camera system. The five trials with the least amount of marker exclusion were chosen for analysis. The inverse kinematics and dynamics for each trial were determined using OpenSim and then filtered through a low pass filter. The resulting ranges of motion for shoulder elevation and elbow flexion for both the drinking and pointing tasks were as expected. For the drinking task, the majority of subjects flexed their elbow 60 degrees and their shoulder elevation fluctuated between 30-50 degrees. For the pointing task, the majority of the subjects elbow flexion varied no more than 10 degrees, and their shoulder elevation fluctuated between 70-95 degrees. Figure 1 shows the results of one of the subjects. From this preliminary study, it is observed that most of the subjects use similar kinematics to accomplish each task; however, several of the subjects displayed irregular kinematics during some of their trials. Overall, these results are consistent with previous studies [1]. This preliminary study provides a foundation for future studies focusing on injury prevention and performance enhancement for athletes. A range of functional movements will be recorded and analyzed to develop quantitative metrics that will serve as indicators for potential injuries and will also be used to inform training protocols aimed at reducing the overall risk of injuries for athletes

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