Name
Abstract | ||
|---|---|---|
Analysis and recognition of motion patterns from data acquired by body-worn inertial sensors is an emerging technology in sports. In this paper we propose an effective method for recognition of fencing footwork using a single body-worn accelerometer. We present a challenging dataset consisting of six actions, which were performed by ten persons and repeated ten times by each of them. We propose a segment-based SVM for time-series classification together with a set of informative features. We demonstrate that the method is competitive with 1-NN DTW in terms of classification accuracy. The proposed method achieves classification accuracy slightly better than 70% on the fencing footwork dataset. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/SPA.2016.7763586 | 2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) |
Keywords | Field | DocType |
Activity recognition,sport sciences,time series,signal processing,fencing | Data modeling,Computer vision,Fencing,Accelerometer,Effective method,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Inertial measurement unit | Conference |
ISSN | ISBN | Citations |
2326-0262 | 978-1-5090-2661-6 | 0 |
PageRank | References | Authors |
0.34 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Filip Malawski | 1 | 6 | 3.19 |
| Bogdan Kwolek | 2 | 328 | 40.16 |