Biomechanical Indicators of Steeplechase Hurdle Success

The steeplechase is a long-distance running event that requires competitors to jump over 28 hurdles and 7 water jumps over the course of the race. This frequent jumping means that hurdling technique is important and the ability to maintain speed over the barriers can help a runner succeed. PURPOSE: To determine which variables predict maintenance of speed while hurdling in the steeplechase. METHODS: Data were collected at the USATF outdoor championships and Olympic Trials from 2011 to 2023 for both men and women. A Sony video camera running at 120 Hz was used to evaluate several aspects of the runner’s mechanics as well as their horizontal velocity before jumping and after landing. The ratio of exit to approach velocity was taken and used as our measure of how successful the jump was, a ratio closer to one means they lost less velocity when jumping over the hurdle. A stepwise linear regression was done for both men and women and was used to determine which variables best predicted hurdle success. RESULTS: Men and women had slightly different variables that predicted successful hurdling. The model for women had an R2 of 0.179 (p \u3c 0.001). For men the R2 was 0.060 (p\u3c0.001). Both models included increased takeoff distance and greater knee flexion angle at takeoff as beneficial. Both models also included the lead knee extension when going over the hurdle, but it was a negative relationship in women and a positive relationship in men. The model for the men also included a less extended hip at takeoff. The model for the women added the clearance of the hip over the hurdle. CONCLUSION: Coaches should focus on having athletes take off a little farther from the barrier and working to have a more flexed knee at takeoff. Men and women have differing hurdling techniques in the steeplechase. While some of the same variables are important, there are also distinct differences. When coaching athletes these differences in technique should be accounted for

Biomechanical Indicators of Water Jump Performance

During the course of the steeplechase track event athletes pass through one water jump obstacle per each of seven laps. There are many different elements of technique that can be used to improve maintenance of horizontal velocity through each obstacle. PURPOSE: This study investigated which biomechanical factors were correlated with higher ratios of exit velocity to approach velocity while negotiating the water jump obstacle. METHODS: Biomechanical data were gathered from the steeplechase event for both men and women at the USATF Outdoor Championships and Olympic Trials. Data were included from 2011 through 2023. Biomechanical data were measured from recorded video using Dartfish video analysis software. Knee and hip angles, time of stepping on the barrier, and take off and landing distances were measured at key points of the movement along with approach and exit velocities. These velocities were measured through 2m sections prior to the barrier and after leaving the water pit. A stepwise linear regression tested for correlations between the exit to approach velocities to a variety of biomechanical measurements. RESULTS: The predictor variables for both men and women were the same, including: landing distance, pushoff angle, and barrier time normalized to average velocity (Women R2=0.290, p2=0.236, pCONCLUSION: According to our data, steeplechase athletes can improve horizontal velocity maintenance through the water jump obstacle by landing further from the barrier into the water, extending more at the knee while pushing off the barrier, and spending less time on the barrier. While previous research showed women lose more velocity during the water jump, the correlated factors were the same and were even entered into the model in the same order showing coaches and athletes the importance of where to focus their technique improvements

Effect of Air Resistance on Braking and Propulsive Impulses During Treadmill Running.

Treadmill running is a convenient option for runners looking to avoid adverse environmental conditions or that prefer a gym setting. Outdoor running includes air resistance, whereas treadmill running typically does not. Very little research has been focused on the influence of air resistance and its role on kinetic factors during running. PURPOSE: To determine how anterior/posterior impulses change due to air resistance during two different treadmill speeds. METHODS: A wind tunnel was placed 0.61m from the edge of a force instrumented treadmill (Bertec, Boston, MA) while attempting to run 1.12m from the opening of it. Seven subjects ran at two speeds (3.35 m/s, 4.46 m/s) on two separate visits while alternating the order of speeds run. During each speed, runners completed one minute of running during conditions of no fan and a fan representing air resistance equal to treadmill speed. Forces were collected for the final 25s segment of each air velocity. RESULTS: At the faster treadmill speed, horizontal impulse was significantly greater in the propulsive direction during the air resistance condition (5.3% ± 7.4%, p=0.019). Braking impulses were smaller (-3.2% ± 5.1%, p=0.035) while propulsive impulse remained non-significant (2.1% ± 4.5%, p=0.104). At the slower treadmill speed, horizontal impulse was trending toward significance (3.1% ± 5.9%, p=0.080) while braking impulse remained non-significant (-1.2% ± 2.8%, p=0.147) and propulsive impulse was greater with air resistance (2.3% ± 3.3%, p=0.024). CONCLUSION: The current data begins to explain that in order to keep metabolic costs low while still compensating for air resistance during running, individuals will increase net horizontal impulse by opting to decrease braking impulse while maintaining propulsive impulse. These findings match the work of Chang and Kram (2000) who asserted that “the metabolic cost of generating horizontal propulsive forces during normal running constitutes more than one-third of the total cost of steady-speed running”

Book Reviews

Book Review 1Book Title: Waders of southern AfricaBook Author: Phil Hockey (Illustrated by Claire Douie)Struik Winchester. Cape Town. 1995. 288 pp.Book Review 2Book Title: Apple Snails in the AquariumBook Author: Gloria Perera & J.G.WallsPublished by T.F.H. Publications, Neptune, New Jersey 07753. (1996).Book Review 3Book Title: The African Leopard: Ecology and Behavior of a Solitary FelidBook Author: Theodore N. BaileyColumbia Universily Press, New York. 1993. xviii + 429pp. ISBN 0-231-07872-2 (cloth)

Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction

Radiomics is the high-throughput extraction and analysis of quantitative image features. For non-small cell lung cancer (NSCLC) patients, radiomics can be applied to standard of care computed tomography (CT) images to improve tumor diagnosis, staging, and response assessment. The first objective of this work was to show that CT image features extracted from pre-treatment NSCLC tumors could be used to predict tumor shrinkage in response to therapy. This is important since tumor shrinkage is an important cancer treatment endpoint that is correlated with probability of disease progression and overall survival. Accurate prediction of tumor shrinkage could also lead to individually customized treatment plans. To accomplish this objective, 64 stage NSCLC patients with similar treatments were all imaged using the same CT scanner and protocol. Quantitative image features were extracted and principal component regression with simulated annealing subset selection was used to predict shrinkage. Cross validation and permutation tests were used to validate the results. The optimal model gave a strong correlation between the observed and predicted shrinkages with . The second objective of this work was to identify sets of NSCLC CT image features that are reproducible, non-redundant, and informative across multiple machines. Feature sets with these qualities are needed for NSCLC radiomics models to be robust to machine variation and spurious correlation. To accomplish this objective, test-retest CT image pairs were obtained from 56 NSCLC patients imaged on three CT machines from two institutions. For each machine, quantitative image features with concordance correlation coefficient values greater than 0.90 were considered reproducible. Multi-machine reproducible feature sets were created by taking the intersection of individual machine reproducible feature sets. Redundant features were removed through hierarchical clustering. The findings showed that image feature reproducibility and redundancy depended on both the CT machine and the CT image type (average cine 4D-CT imaging vs. end-exhale cine 4D-CT imaging vs. helical inspiratory breath-hold 3D CT). For each image type, a set of cross-machine reproducible, non-redundant, and informative image features was identified. Compared to end-exhale 4D-CT and breath-hold 3D-CT, average 4D-CT derived image features showed superior multi-machine reproducibility and are the best candidates for clinical correlation

Electric Vehicles

This chapter regards the current state of electric vehicles in society today: the pros and cons, areas that need to improve, etc. The chapter starts by discussing some of the unethical practices that go into creating commodities of electric vehicles. From there, we explore the short, yet rapidly changing history of electric vehicles. Topics explored throughout its history include early inefficiencies, the introduction of hybrids that led to major improvements, and the increase in availability. The chapter then explores markets and commodities of electric vehicles, which explores the supply and demand side of this technology. We will show how growing demand has lead to improved government action, and how production methods must be improved for a sustainable future of electric vehicles. Finally, we test the ethics of electric vehicles, challenging the notion of nothing but the most ethical environmental standards. We will show how ways of metal mining and electricity generation actually do more bad than good for our environment. By reading this chapter, you will become more knowledgeable of how impactful electric vehicles are, and also you will get a grasp on whether or not you want to support this practice.Angela Person, Ph.D.N

Joint Angle Calculations using Motion Capture and Deep Learning Pose Estimation while Running

Marker based motion capture is currently the most accurate method of measuring human kinematics; however, it is expensive and is often limited to lab environments making it unsuitable for many applications. Two-dimensional methods are available through open source code, but it is unclear which of these methods provides the greatest accuracy. PURPOSE: The purpose of this study is to quantify the accuracy of pose estimation from a monocular electro-optical sensor with deep learning to infer segment end points and pose estimation utilizing two open-source code approaches. METHODS: One subject ran at 6.5 m/s for 15 s while being recorded with Vicon Nexus and an iPhone both running at 240 Hz. Visual 3D computed joint angles from the marker data. The iPhone view was placed perpendicular to the sagittal plane. Deep learning algorithms produced 2D pose information that was translated into hip, knee, and ankle sagittal plane joint angles. Pearson r correlations compared MediaPipe and OpenPose joint angle estimations through 15 s of running to the motion capture data. RESULTS: Markerless methods showed correlation values compared with Visual 3D of hip (MediaPipe = 0.968, OpenPose = 0.975), knee (MediaPipe = 0.983, OpenPose = 0.964), and ankle (MediaPipe = 0.928, OpenPose = 0.904). Both markerless methods showed limitations on predicting maximum flexion and extension angles. Although the correlation values were high, in practice these differences in maximum range of motion may impact any future interpretation of data. CONCLUSION: Care should be taken when utilizing extreme joint angles when using deep learning algorithms. Although at this point the open source methods are not as accurate as marker based motion capture they could enable the collection of data from a larger population of people given the ease of data collection, this could facilitate crowd sourced data collection with much larger sample sizes than are traditionally feasible

Confidently rule out CAP in the outpatient setting

Confidently rule out CAP in the outpatient setting. A focus on specific signs and symptoms -- without imaging -- may rule out community-acquired pneumonia in outpatients. PRACTICE CHANGER: You can safely rule out community-acquired pneumonia (CAP) -- without requiring a chest x-ray -- in an otherwise healthy adult outpatient who has an acute cough, a normal pulmonary exam, and normal vital signs using this simple clinical decision rule (CDR). STRENGTH OF RECOMMENDATION: A: Based on a systematic review of prospective case-control studies and randomized controlled trials in the outpatient setting.Timothy Mott, MD; David Echeverri, MD; Luke Fondren, DO; Ashley Hunter, MD (South Baldwin Regional Medical Center Family Medicine Residency, Foley, AL)Includes bibliographical reference

HARPS: An Online POMDP Framework for Human-Assisted Robotic Planning and Sensing

Autonomous robots can benefit greatly from human-provided semantic characterizations of uncertain task environments and states. However, the development of integrated strategies which let robots model, communicate, and act on such 'soft data' remains challenging. Here, the Human Assisted Robotic Planning and Sensing (HARPS) framework is presented for active semantic sensing and planning in human-robot teams to address these gaps by formally combining the benefits of online sampling-based POMDP policies, multimodal semantic interaction, and Bayesian data fusion. This approach lets humans opportunistically impose model structure and extend the range of semantic soft data in uncertain environments by sketching and labeling arbitrary landmarks across the environment. Dynamic updating of the environment model while during search allows robotic agents to actively query humans for novel and relevant semantic data, thereby improving beliefs of unknown environments and states for improved online planning. Simulations of a UAV-enabled target search application in a large-scale partially structured environment show significant improvements in time and belief state estimates required for interception versus conventional planning based solely on robotic sensing. Human subject studies in the same environment (n = 36) demonstrate an average doubling in dynamic target capture rate compared to the lone robot case, and highlight the robustness of active probabilistic reasoning and semantic sensing over a range of user characteristics and interaction modalities