Technical Note on Pickup Acceleration Signal Processing.

A custom MATLAB script successfully identified critical steps and calculated step-specific variables from motorized linear position encoder technology during pickup acceleration sprints.

• Variables calculated included step velocity, acceleration, horizontal force, and step length
• The analysis captured approach steps (-2, -1), a transition step (0), and pickup steps (1, 2) across the acceleration phase
• Fifteen trained athletes (22.6 ± 5.3 years) from diverse sporting backgrounds served as participants
• Athletes performed 30-m sprints at controlled entry velocities of 1.5 and 3.0 m·s⁻¹

Step-by-step analysis of pickup acceleration can be effectively captured in field settings using motorized linear position encoder technology.

• The method provides mechanical insights that were previously limited to laboratory environments
• The approach is described as a 'practical and portable solution for assessing sport-specific sprint mechanics'
• The method enables individualized training prescriptions based on step-specific mechanical profiles
• Two controlled entry velocities (1.5 and 3.0 m·s⁻¹) were used to simulate sport-relevant dynamic starting conditions

The motorized linear position encoder method for pickup acceleration analysis has identified limitations including low-load resistance and tether oscillation.

• Low-load resistance of 1 kg was noted as a limitation of the approach
• Tether oscillation was identified as an additional limitation
• Future research should establish the reliability of step-derived measures
• Future research should also explore targeted interventions for improving pickup acceleration performance in team sport athletes

Pickup acceleration from dynamic positions is described as fundamental to team sports performance yet remains under-researched.

• The study focused on customizing methods for extracting step-specific variables from motorized linear position encoder technology
• The study population consisted of 15 trained athletes from diverse sporting backgrounds
• Mean athlete age was 22.6 ± 5.3 years
• The research addresses a gap by providing a portable, field-based alternative to laboratory assessment of sprint mechanics