{"title":"Analysis of Plantar Pressure Image Based on Flexible Force-Sensitive Sensor Array","authors":"Bochen Li, Zhiming Yao, Jianguo Wang, Shaonan Wang, Qi Wu, Peng Wang, Xianjun Yang","doi":"10.1109/ISCID51228.2020.00079","DOIUrl":null,"url":null,"abstract":"The flexible force-sensitive sensor array can obtain the plantar pressure distribution information during walking, which has important clinical value. In this study, we proposed a method for analyzing plantar pressure images based on prior knowledge. The proposed method includes data preprocessing, footprint recognition and segmentation, and stride analysis. First, the clustering algorithm was used to extract the footprints; Then, the footprints were recognized based on shape features and segmented based on foot anatomical features. Finally, the least square method was used to stride analysis. Experimental results show that the proposed method has good performance in footprint recognition and segmentation. It is expected to be applied to clinical auxiliary diagnosis.","PeriodicalId":236797,"journal":{"name":"2020 13th International Symposium on Computational Intelligence and Design (ISCID)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 13th International Symposium on Computational Intelligence and Design (ISCID)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCID51228.2020.00079","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The flexible force-sensitive sensor array can obtain the plantar pressure distribution information during walking, which has important clinical value. In this study, we proposed a method for analyzing plantar pressure images based on prior knowledge. The proposed method includes data preprocessing, footprint recognition and segmentation, and stride analysis. First, the clustering algorithm was used to extract the footprints; Then, the footprints were recognized based on shape features and segmented based on foot anatomical features. Finally, the least square method was used to stride analysis. Experimental results show that the proposed method has good performance in footprint recognition and segmentation. It is expected to be applied to clinical auxiliary diagnosis.