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Application of functional data analysis to explore movements: walking, running and jumping - A systematic review
Julia Dannenmaier1, Christina Kaltenbach1, Theresa Kölle1
1Institute for Research in Rehabilitation Medicine at Ulm University (IFR Ulm), Bad Buchau, Germany.
Gait & Posture
|February 15, 2020
Summary
Functional Data Analysis (FDA) offers a comprehensive approach to analyzing continuous motion data, providing deeper insights into lower limb movement patterns. This method is increasingly vital for understanding complex biomechanical signals.
Area of Science:
- Biomechanics
- Data Science
- Orthopedics
Background:
- Traditional statistical analysis often overlooks continuous motion data.
- Functional Data Analysis (FDA) enables the examination of entire signals over time.
- FDA is emerging as a powerful tool for analyzing complex biomechanical data.
Purpose of the Study:
- To review the application of Functional Data Analysis (FDA) in the study of lower limb movements.
- To understand how FDA is utilized in analyzing motion-associated variables.
- To assess the growing importance of FDA in biomechanical research.
Main Methods:
- Literature search conducted across EMBASE, PubMed, and Ovid MEDLINE.
- Inclusion of articles applying FDA to trajectories, ground reaction force, and electromyography.
- Independent assessment of references by two reviewers.
Main Results:
- 13 articles published between 2009-2019 met the inclusion criteria.
- Studies examined healthy individuals and patients with various lower limb conditions.
- FDA techniques like functional principal component analysis and functional ANOVA were employed.
Conclusions:
- Functional Data Analysis provides detailed insights into motion patterns.
- The increasing number of publications highlights the growing significance of FDA.
- FDA is crucial for the explorative analysis of continuous movement data.
