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Combined statistical study of joint angles and ground reaction forces using component and multiple correspondence
P Loslever1, E M Laassel, J C Angue
1Laboratoire d'Automatique Industrielle et Humaine, Université de Valenciennes et du Hainaut Cambresis, France.
IEEE Transactions on Bio-Medical Engineering
|December 1, 1994
Summary
This study introduces a new method for analyzing gait patterns using hip, knee, and ankle movements, and ground reaction forces. The approach creates visual gait patterns to track rehabilitation progress.
Area of Science:
- Biomechanics
- Human Movement Analysis
- Data Science
Background:
- Gait analysis is crucial for understanding human locomotion and diagnosing movement disorders.
- Existing methods may not fully capture the complex, multidimensional nature of gait dynamics.
Purpose of the Study:
- To propose a general methodology for creating comprehensive gait patterns.
- To integrate kinematic (joint angles) and kinetic (ground reaction forces) data.
- To develop a tool for assessing gait and monitoring rehabilitation.
Main Methods:
- Multidimensional signal analysis involving sliding averages and time windows.
- Principal Component Analysis (PCA) for dimensionality reduction of six gait signals.
- Multiple Correspondence Analysis (MCA) on fuzzy modalities to identify gait patterns.
Main Results:
- Generation of gait patterns integrating temporal and spatial aspects.
- Development of factor planes from MCA as data models.
- Demonstration of projecting new gait data (e.g., pathological) onto these models.
Conclusions:
- The proposed methodology provides a novel way to visualize and analyze gait.
- The generated gait patterns can serve as reference models for normal and pathological gait.
- This approach facilitates the objective assessment of rehabilitation progress by comparing subject data to established patterns.