Automated plantar contact area estimation in a dynamic state using K-Means clustering

Abstract

Background

Estimation of plantar contact area (PCA) can be used for a variety of purposes such as classification of foot types and diagnosis of foot abnormalities. While some techniques have been developed for assessing static PCA, understanding dynamic PCA may improve understanding of gait biomechanics. This study aims (1) to develop an approach to estimate PCA from video images of footprints during walking and (2) to assess the accuracy and generalizability of this method.

Methods

A sample of 41 ambulatory, young adults (age = 24.3 ± 3.2 years, mass = 67.2 ± 16.9 kg, height = 1.63 ± 0.08 m) completed 10 trials walking on a raised transparent plexiglass platform. Foot contact during walking was recorded using a video camera placed under the platform. An image processing algorithm, Clustering Segmentation, was developed based on identifying color intensity between the PCA and the rest of the foot and plantar contact morphology.

Results

The proposed approach was compared to manual hand tracing, which is widely accepted as the Gold Standard, as well as with an earlier automated approach (Lidstone et al., 2019). Results showed that Clustering Segmentation followed the Gold Standard closely in all phases of gait. The maximum PCA and the maximum PCA length and width generally increased with foot size, indicating that the algorithm could successfully estimate the PCA across a wide range of foot sizes. Results also showed that the proposed approach for obtaining the PCA may be used to characterize various foot types in a dynamic state.

Conclusion

Clustering Segmentation algorithm eliminates the need for subjective interpretation of the PCA. The results showed that the algorithm was considerably faster and more accurate than the earlier automated method. The proposed algorithm will be appropriate for assessment of foot abnormalities and provides complementary information to gait analysis.