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Marker-based classification of young-elderly gait pattern differences via direct PCA feature extraction and SVMs
Bjoern M Eskofier1, Peter Federolf, Patrick F Kugler
1Digital Sports Group, Pattern Recognition Laboratory (Computer Science 5), Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, Haberstrasse 2 , 91058, Erlangen, Germany. bjoern.eskofier@informatik.uni-erlangen.de
This study introduces a novel gait analysis method using 3D marker data to classify differences between young and elderly individuals. The approach achieved 95.8% accuracy, offering a powerful tool for injury diagnosis and fall risk assessment.
Area of Science:
- Biomechanics
- Biomedical Engineering
- Data Science
Background:
- Gait pattern classification is crucial for diagnosing injuries and identifying fall risks in the elderly.
- Existing methods often rely on conventional features, limiting comprehensive analysis.
- 3D marker trajectory data offers rich spatial and temporal information for gait analysis.
Purpose of the Study:
- To present a novel method for classifying group differences in gait patterns.
- To utilize complete spatial and temporal information from marker motion.
- To compare classification rates with previous studies using conventional features.
Main Methods:
- Collected 37 3D marker trajectories from 24 young and 24 elderly female subjects walking on a treadmill.
- Applied Principal Component Analysis (PCA) to retain spatial and temporal marker information.
- Utilized a Support Vector Machine (SVM) with a linear kernel for classification.
Main Results:
- Achieved a high classification rate of 95.8% for differentiating gait patterns between young and elderly groups.
- Enabled visualization of individual marker contributions to group differentiation in both position and time.
- Demonstrated an approach that requires no specific assumptions or prior knowledge of gait cycle time points.
Conclusions:
- The proposed method effectively classifies group differences in gait patterns using comprehensive marker data.
- This approach offers a direct and applicable tool for group classification tasks in studies involving marker measurements.
- The method's ability to visualize marker contributions enhances understanding of gait variations and potential risks.
