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Effect of sparsity-aware time–frequency analysis on dynamic hand gesture classification with radar micro-Doppler signatures
| Content Provider | Semantic Scholar |
|---|---|
| Author | Li, Gang Zhang, Shimeng Fioranelli, Francesco Griffiths, Hugh |
| Copyright Year | 2018 |
| Abstract | Dynamic hand gesture recognition is of great importance in human-computer interaction. In this study, the authors investigate the effect of sparsity-driven time-frequency analysis on hand gesture classification. The time-frequency spectrogram is first obtained by sparsity-driven time-frequency analysis. Then three empirical micro-Doppler features are extracted from the time-frequency spectrogram and a support vector machine is used to classify six kinds of dynamic hand gestures. The experimental results on measured data demonstrate that, compared to traditional time-frequency analysis techniques, sparsity-driven time-frequency analysis provides improved accuracy and robustness in dynamic hand gesture classification. |
| Starting Page | 815 |
| Ending Page | 820 |
| Page Count | 6 |
| File Format | PDF HTM / HTML |
| DOI | 10.1049/iet-rsn.2017.0570 |
| Volume Number | 12 |
| Alternate Webpage(s) | http://ancortek.com/wp-content/uploads/2019/05/2018-06.pdf |
| Alternate Webpage(s) | http://eprints.gla.ac.uk/160032/7/160032.pdf |
| Alternate Webpage(s) | https://doi.org/10.1049/iet-rsn.2017.0570 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
