Name
Title | ||
|---|---|---|
A Data-driven Methodology to Characterize the Dynamic Pattern of Human Motion Based on Plantar Pressure Measurements |
Abstract | ||
|---|---|---|
Foot plantar pressure measurements provide a window into the analysis of gait and posture and offer valuable insights into movement quality that are used in the health, sports and commodities (e.g. shoes) sectors. In this paper, we propose a novel data-driven methodology to profile subjects performing a running exercise and to identify groups with unique gait characteristics. The methodology quantifies trial-base similarities using dynamic time warping and, thereby, group different trials based on their precise spatial and temporal dynamics. The characterization of each group revealed the existence of strikingly different pressure profiles. This methodology opens the possibility to develop accurate benchmarking and prediction algorithms on homogeneous groups of running profiles and enables the followup in time of the evolution of pressure patterns during movement execution. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IS48319.2020.9200183 | 2020 IEEE 10th International Conference on Intelligent Systems (IS) |
Keywords | DocType | ISBN |
plantar pressure,dynamic time warping,clustering,motion profiling,data standardization | Conference | 978-1-7281-5456-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Henrique Cabral | 1 | 0 | 0.34 |
| Tom Tourwé | 2 | 0 | 0.34 |
| Elena Tsiporkova | 3 | 0 | 0.34 |
| Wouter Aerts | 4 | 0 | 0.34 |