Research on feature extraction algorithm for plantar pressure image and gait analysis in stroke patients

• Published in: Journal of visual communication and image representation Vol. 58; pp. 525 - 531
• Main Authors: Wang, Mo, Wang, Xin'an, Fan, Zhuochen, Chen, Fei, Zhang, Sixu, Peng, Chen
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.01.2019
• Subjects: Clustering analysis; Feature extraction; Gait analysis; Image denoising; Plantar pressure; COP; Feature extraction; Clustering analysis; DBSCAN; Plantar pressure; Gait analysis; Image denoising
• ISSN: 1047-3203; 1095-9076
• DOI: 10.1016/j.jvcir.2018.12.017

The plantar pressure image is an important tool for gait analysis. It has important applications in evaluating the recovery of stroke patients after operation and formulating the rehabilitation training program. It is one of the key technologies of gait analysis to extract foot feature parameters from static/dynamic plantar pressure images. This article deals with the noise in the original image through the piecewise linear grayscale transformation, the time domain mean filter and the maximum value filter, then determine the position of the feet in the image by the foot localization algorithm based on the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and the K-means clustering method. Finally, the plantar pressure feature parameters were extracted according to the positioned images. Based on the above feature parameter extraction algorithm, the plantar pressure feature parameters of 20 healthy subjects and 20 S patients with relative recovery period (2–6 months after the onset) were compared, showing a statistically significant difference (P < 0.001). Based on the above data, gait characteristics of stroke patients were further analyzed.