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”

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

Biomechanical Changes in Running Post-Transition in a Triathlon

Triathletes often complain about lower limb discomfort when running after cycling (Quigley, 1996). Several studies have found differences in muscle activation (Chapman, 2009), kinematics (Rendos et al., 2013), and kinetic cost (Millet, 2001) during the transition run. These differences were also found to be more severe in less experienced triathletes (Chapman, 2008). PURPOSE: This study aimed to determine the kinetic differences between baseline and transition runs of inexperienced triathletes. METHODS: Twelve novice triathletes age: 29.4 ± 12.15 y, mass: 71.2 ± 10.3 kg, weekly running mileage: 24.4 ± 16.7 mi/week volunteered to participate. Athletes completed a 20 min run during session 1, and a 20 min bike followed by a 20 min run during session 2, each conducted at 75-80% effort level. Cycling sessions occurred on a stationary trainer (Wahoo Kickr snap) allowing athletes to use their own bike or have a standard bike fitted to their preferred geometry. Running sessions occurred on an outdoor loop (~370m) where subjects passed through timing gates (Brower) and over two force plates (Kistler, 1200 Hz). Sagittal view, right side video (Sony, 240 Hz) was also collected. Each running session was broken into 4 five-minute blocks. Duration of foot contact with the ground determined stance phase, while braking (-RFY) and propulsive phases (+RFY) were also defined. A two-way repeated measures ANOVA was used to determine differences between run type and time block (α = 0.05). RESULTS: During the stance phase, there was a main effect for time block of anterior linear impulse (F = 3.03, p = 0.043) and average +RFY (main effect F = 3.37, p = 0.03) and contact time (F = 3.11, p = 0.039). Similarly, there was a main effect for time of propulsive phase linear impulse (F = 7.94, p \u3c 0.001) and average +RFY (F = 7.95, p \u3c 0.001). Although post-hoc analysis did not reveal significant differences, it appears the later time blocks decreased anterior linear impulse as RFY decreased. CONCLUSIONS: Athletes displayed similar running kinetics between the baseline and transition run, however differences did occur between time blocks. This suggests that athletes fatigued similarly between the two run types. These findings may indicate that self-reported discomfort in the transition run may not be detected by measures of whole-body kinetics