Summer Track Block Start Research
Track and field sprinting demands exceptional precision, with milliseconds often deciding victories. This research investigates how varying the placement of starting blocks influences sprint start biomechanics, addressing gaps in prior studies that did not explore front block placement variations. Using OpenCap for motion capture and OpenSim for musculoskeletal analysis, athletes performed six randomized starts with the front block set at 45%, 57.5%, and 70% of their leg length from the starting line, while maintaining a consistent block spacing of 45% leg length.
A custom Python program played a pivotal role in analyzing the data, extracting insights on metrics such as forward velocity, second-step distance, and center of mass position. The program aggregated results, calculated averages, generated visualizations, and facilitated statistical comparisons, ensuring a robust and streamlined analysis pipeline.
Findings suggest that a middle front block placement (57.5%) optimizes performance, though the data from four subjects revealed no statistically significant differences there were visually significant ones. With even minor improvements holding practical value in a sport measured by milliseconds, this work provides actionable insights.
Tools Used
Visual Studio Code- This served as my development environment.
Python- Was used with several libraries like NumPy to take in large CSV of number and generate meaningful averages and graphs. Open Cap- Was used to perform motion capture. This platform developed at Stanford that uses synchronized recordings from multiple devices, such as iPads, to create 3D models of human motion for biomechanical analysis.
Open Sim- Was used to gather further data and is an open-source software platform for modeling, simulating, and analyzing the musculoskeletal system, enabling researchers to study joint angles, forces, and movements derived from motion capture data.
Python- Was used with several libraries like NumPy to take in large CSV of number and generate meaningful averages and graphs. Open Cap- Was used to perform motion capture. This platform developed at Stanford that uses synchronized recordings from multiple devices, such as iPads, to create 3D models of human motion for biomechanical analysis.
Open Sim- Was used to gather further data and is an open-source software platform for modeling, simulating, and analyzing the musculoskeletal system, enabling researchers to study joint angles, forces, and movements derived from motion capture data.