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Sample size estimation in locomotion kinematics and electromyography for statistical parametric mapping.

Francesco Luciano1, Luca Ruggiero1, Gaspare Pavei1

  • 1Department of Pathophysiology and Transplantation, University of Milan, Italy.

Journal of Biomechanics
|May 2, 2021
PubMed
Summary

Researchers established benchmarks for sample size estimation in biomechanics, improving statistical power in one-dimensional (1D) hypothesis tests for locomotion studies. This aids in obtaining reliable kinematic and electromyographic data analysis.

Keywords:
1D hypothesis testingCerebral palsyRunningSPMWalking

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Area of Science:

  • Biomechanics
  • Statistical analysis
  • Locomotion studies

Background:

  • Kinematic and electromyographic (EMG) data in biomechanics are often 1D waveforms analyzed using 1D hypothesis tests.
  • These statistical methods are crucial for reliable locomotion research, yet a priori sample size estimation is frequently overlooked.
  • Statistical power, the ability to detect true effects, can be calculated for 1D tests using statistical parametric mapping.

Purpose of the Study:

  • To characterize parameters influencing sample size estimation in locomotion studies.
  • To determine how these parameters impact statistical power in 1D hypothesis tests.
  • To provide benchmarks for sample size estimation in 1D locomotion data analysis.

Main Methods:

  • Defined noise and signal characteristics in kinematic and EMG data from physiological and pathological locomotion.
  • Conducted 1D power analysis under representative conditions.
  • Generated a dataset of tabulated sample sizes based on noise and signal parameters.

Main Results:

  • Kinematic and EMG data exhibited smooth Gaussian noise, with characteristics varying by condition and anatomical site.
  • Statistical power increased with higher signal amplitude and signal full-width-at-half-maximum.
  • Power was enhanced by defining a region of interest and employing paired over unpaired study designs.

Conclusions:

  • Established initial benchmarks for appropriate sample sizes in 1D hypothesis testing for locomotion.
  • Demonstrated factors influencing statistical power, aiding in the evaluation of 1D tests.
  • Provides guidance for sample size estimation in biomechanical locomotion research.