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Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
Published on: March 4, 2018
A novel approach to quantify time series differences of gait data using attractor attributes
Manfred M Vieten1, Aida Sehle, Randall L Jensen
1Department of Sport Science, University of Konstanz, Konstanz, Germany. manfred.vieten@uni-konstanz.de
Plos One
|August 17, 2013
Summary
This study introduces a novel method for analyzing time series data using limit cycle attractors. The new technique effectively quantifies gait pattern differences across various conditions, offering a reliable diagnostic tool.
Area of Science:
- Biomechanics
- Data Analysis
- Time Series Analysis
Background:
- Quantifying differences in time series data, particularly with underlying limit cycle attractors, is crucial for understanding dynamic systems.
- Existing methods for gait classification can be expensive and time-consuming.
Purpose of the Study:
- To introduce a new method for quantifying differences in time series data using live/corporeal data and limit cycle attractors.
- To apply this method to gait data for identifying and classifying gait pattern differences.
- To assess the reliability and effectiveness of the new method in differentiating various walking conditions.
Main Methods:
- Approximation of limit cycle attractors from time series data.
- Calculation of three key measures: δM (attractor difference), δD (deviation difference), and δF (combined index).
- Application to gait data from treadmill walking under normal, dual-task, and weighted conditions.
Main Results:
- The new method successfully differentiated between the three walking conditions.
- Excellent reliability (ICC(ave) > 0.73) was found for δM across days.
- Good reliability (ICC(ave) = 0.414 to 0.610) was found for δD across days.
Conclusions:
- The proposed method is a reliable and effective tool for quantifying gait pattern differences.
- It offers an accessible alternative to existing, more complex gait classification techniques.
- The method shows potential as a diagnostic tool for assessing clinical changes, especially in neurological patients.
