Application of multidimensional gait feature fusion algorithm in gait assessment for patients with knee osteoarthritis.

Background: To address the limitation in gait assessment for patients with knee osteoarthritis (KOA) and after total knee arthroplasty (TKA), where it is difficult to simultaneously quantify joint dynamic coordination and movement complexity, a multidimensional gait feature fusion algorithm is proposed. Methods: Spatial motion data were collected from 70 participants (21 healthy controls, 24 KOA patients, and 25 post-TKA patients) using a 3D motion capture system. Hip-knee cyclograms were constructed to extract morphological features (centroid, range of motion, perimeter, and area) for quantifying dynamic coordination, while sample entropy of hip, knee, and ankle joint angles was calculated to quantify movement complexity. Features were categorized into four input types: fused multidimensional features, cyclogram morphological features, sample entropy features, and traditional spatiotemporal parameters. Machine learning models including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), and k- Nearest Neighbors (KNN) were employed for gait classification and assessment. Results: Multidimensional feature analysis revealed a characteristic pathological compensation pattern of "decreased cyclogram features with increased sample entropy" in the KOA group, while the TKA group demonstrated postoperative improvements in both dimensions. The incorporation of multidimensional features significantly enhanced the performance of all classification models: under multidimensional feature input, RF, SVM, DT, and KNN achieved accuracies of 96.93%, 92.44%, 90.29%, and 88.98%, respectively-all significantly outperforming models using single-dimensional features. Conclusion: The multidimensional gait feature fusion algorithm effectively overcomes the limitation of assessing either coordination or complexity in isolation, providing an interpretable quantitative tool for analyzing KOA pathological mechanisms and dynamically monitoring post-TKA rehabilitation.