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Multifractal Multiscale Analysis of Human Movements during Cognitive Tasks.

Andrea Faini1,2, Laurent M Arsac3, Veronique Deschodt-Arsac3

  • 1Department of Cardiovascular, Neural and Metabolic Sciences, Istituto Auxologico Italiano, IRCCS, 20149 Milan, Italy.

Entropy (Basel, Switzerland)
|February 23, 2024
PubMed
Summary
This summary is machine-generated.

Multiscale multifractal analysis (MFMS-DFA) effectively detects cognitive load effects on human movement dynamics. Collaborative gameplay significantly reduced cognitive load impacts on pedal control, highlighting MFMS-DFA

Keywords:
Legendre spectrumTetriscyclingdetrended fluctuation analysismultifractalmultifractal cumulative functionmultiscale analysis

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Area of Science:

  • Movement Science
  • Complex Systems Analysis
  • Cognitive Neuroscience

Background:

  • Human movement adaptation relies on multifractality, a complex fractal process.
  • Multifractal spectrum analysis can reveal adaptive processes in perception, cognition, and action.
  • Multiscale multifractal analysis based on DFA (MFMS-DFA) was developed for cardiovascular dynamics.

Purpose of the Study:

  • To assess the utility of MFMS-DFA for identifying multiscale structures in human movement dynamics.
  • To investigate how cognitive demand, specifically playing Tetris, affects movement multifractality.
  • To explore the interplay between skill, cognitive demand, and movement control.

Main Methods:

  • Thirty-six participants pedaled under three conditions: reference cycling, cycling while playing Tetris alone, and collaboratively.
  • Pedal revolution period (PRP) series were analyzed using MFMS-DFA.
  • Results were compared against linearized surrogates to confirm multifractality.

Main Results:

  • MFMS-DFA confirmed multifractality in pedal movement dynamics across various scales.
  • Playing Tetris significantly altered multifractality at scales of approximately 16 and 64 seconds.
  • Collaborative Tetris play attenuated these alterations, particularly in skilled players.

Conclusions:

  • MFMS-DFA is sensitive to cognitive demand in human movement dynamics.
  • The method can assess the interplay between skill and task demands.
  • MFMS-DFA shows potential for studying brain-movement coordination and diagnosing neurological disorders.