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Analyzing Force-Time Curves: Comparison of Commercially Available Automated Software and Custom MATLAB Analyses.

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Commercial force plate software shows agreement in some force-time curve metrics, but inconsistencies necessitate careful comparison. Understanding these differences is crucial for accurate analysis in strength and conditioning.

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Area of Science:

  • Biomechanics
  • Sports Science
  • Human Movement Analysis

Background:

  • Automated force-time curve analysis is increasingly common in commercial force plate systems.
  • Understanding the agreement between different software analyses is critical for data interpretation.
  • Previous comparisons have not comprehensively evaluated leading commercial software against custom analysis.

Purpose of the Study:

  • To compare the agreement of force-time curve metrics derived from commercial automated software (Vald Performance, Hawkin Dynamics) and custom MATLAB scripts.
  • To identify systematic and proportional bias between different analysis techniques.
  • To assess the reliability of key biomechanical variables across various jump and pull exercises.

Main Methods:

  • Twenty-four participants performed countermovement jumps, squat jumps, drop jumps, and isometric mid-thigh pulls.
  • Vertical ground reaction forces were analyzed using Vald Performance, Hawkin Dynamics, and custom MATLAB scripts.
  • Analyses were compared using least products regressions, Bland-Altman plots, and percent error, with visual landmark verification.

Main Results:

  • Hawkin Dynamics showed low percent errors (<3%) but exhibited systematic and proportional bias for several metrics.
  • ForceDecks demonstrated larger percent differences and biases, potentially due to variations in movement initiation identification and integration techniques.
  • Peak force for isometric mid-thigh pulls showed high agreement (within 1 N), while metrics like rate of force development were difficult to compare across software.

Conclusions:

  • Many force-time curve metrics show agreement between commercial software and custom analysis when landmarks are visually confirmed.
  • Inconsistencies in analysis procedures (e.g., movement initiation, system weight) can lead to significant errors in subsequent metrics.
  • Researchers should exercise caution and only compare metrics that demonstrate agreement across different software analyses.