Plantar pressure classification and feature extraction based on multiple fusion algorithms.

ABSTRACT: Using multiple fusion algorithms to optimize the classification and feature extraction of plantar pressure during walking stance phase in healthy people, and explore the diversity of plantar pressure distribution. 243 healthy young male individuals was studied to collect data on plantar impulse and maximum pressure indices from ten distinct regions of the foot during walking. Principal component analysis was utilized to reduce the dimensionality of the data. Optimized clustering and feature extraction algorithms categorized the plantar pressure characteristics and extracted key indicators. Classification discriminant functions were developed using linear discriminant analysis. Analysis of variance compared the differences in features between various plantar pressure distribution patterns.

SUBMITTER: Bai X 

PROVIDER: S-EPMC12006501 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature