Bai Xiaotian, Hou Xiao, Song Yiling, Tang Zhengyan, Huo Hongfeng, Liu Jingmin
Department of Physical Education, Tsinghua University, Beijing, China.
School of Sport Science, Beijing Sport University, Beijing, China.
Sci Rep. 2025 Apr 17;15(1):13274. doi: 10.1038/s41598-025-96440-6.
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.
运用多种融合算法优化健康人群步行站立阶段足底压力的分类及特征提取，并探究足底压力分布的多样性。对243名健康年轻男性个体进行研究，收集其步行过程中足部十个不同区域的足底冲量和最大压力指数数据。利用主成分分析降低数据维度。通过优化的聚类和特征提取算法对足底压力特征进行分类并提取关键指标。使用线性判别分析开发分类判别函数。采用方差分析比较不同足底压力分布模式之间的特征差异。通过多种融合算法识别出三种足底压力分布类型，并提取了四个指标，包括第一跖趾关节、第一跖骨、第五跖骨和中足的冲量。原始数据和交叉验证的平均准确率分别为89.70%和88.50%。基于单因素方差分析，最终将分布类型确定为拇趾伸展型、中足-外侧前足蹬离型和正常型。健康人群步行时的足底压力分布可分为拇趾伸展型、中足-外侧前足蹬离型和正常型。其中，第一跖趾关节区域、第五跖骨区域和中足区域周围的冲量表现出较好的分类性能。建议未来的研究结合当前研究结果，采用前瞻性研究进一步分析步态特征与运动损伤之间的关系。
