Plantar pressure classification and feature extraction based on multiple fusion algorithms

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. Three types of plantar pressure distribution were identified by multiple fusion algorithms, and four indicators were extracted, including impulses of Toe1, Meta1, Meta5 and Midfoot. The average accuracy rates of original data and cross-validation were 89.70% and 88.50%. Based on one-way analysis of variance, the distribution types were ultimately determined as thumb extension type, midfoot-lateral forefoot push-off type, and normal type. Plantar pressure distribution during walking in healthy people can be categorized into thumb extension type, midfoot-lateral forefoot push-off type, and normal type. Among them, the impulses around the first metatarsophalangeal joint region, fifth metatarsal bone region and midfoot region showed better classification performance. It is recommended that future studies combine the current findings and use prospective studies to further analyze the relationship between gait characteristics and sports injuries.