Name
Abstract | ||
|---|---|---|
Plantar pressure, an important index in gait analysis, has been widely adopted for clinical diagnosis and sports science. It is of great significance to accurately measure the plantar pressure. In this paper, the data of plantar pressure are collected with the flexible force sensor, and then filtered by a self-designed filtering algorithm with time window. Then, an 8-neighborhood connected-component labeling algorithm was proposed to segment and cluster the plantar pressure images. Finally, the footprints were recognized based on the plantar pressure and shape of footprints. The experimental results show that the proposed footprint extraction method extracted 99% of plantar pressure data accurately under normal walking conditions, and the proposed footprint recognition method recognized more than 97% of footprints in a correct manner. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.18280/ts.360506 | TRAITEMENT DU SIGNAL |
Keywords | Field | DocType |
footprint recognition,plantar pressure,clustering,image segmentation | Computer vision,Plantar pressure,Footprint,Artificial intelligence,Recognition algorithm,Mathematics | Journal |
Volume | Issue | ISSN |
36 | 5 | 0765-0019 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Zhang | 1 | 0 | 0.34 |
| Su Pan | 2 | 0 | 0.34 |
| Yaowu Qi | 3 | 0 | 0.34 |
| Yudan Yang | 4 | 0 | 0.34 |