Name
Abstract | ||
|---|---|---|
Contactless gesture recognition has emerged as a promising technique to enable diverse smart applications, e.g., novel human-machine interaction. Among others, gesture recognition based on radio frequency identification (RFID) is preferred due to its prevalent availability, low cost, and ease in deployment. However, current RFID-based gesture recognition approaches usually use profile template matching to distinguish different gestures, making them suffer from large recognition latency and fail to support real-time applications. In this paper, we propose a real-time and accurate contactless RFID-based gesture recognition approach called ReActor. ReActor uses machine learning rather than time-consuming profile template matching to distinguish different gestures, and thus achieves both very low recognition latency and high recognition accuracy. The major challenge of our approach is to determine a set of suitable attributes that can preserve the profile features of the signals related to different gestures. We combine two types of attributes in ReActor: the statistics of the signal profile that characterize coarse-grained features and the wavelet (transformation) coefficients of the signal profile that characterize fine-grained local features, both of which can be calculated fast. Experimental results demonstrate that ReActor can recognize a gesture with average latency less than 51ms, two orders of magnitude faster than state-of-the-art approaches based on profile template matching. Furthermore, ReActor also achieves higher recognition accuracy than previous works due to its optimized attribute set. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SAHCN.2019.8824853 | 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) |
Keywords | Field | DocType |
gesture recognition,radio frequency identification,real time,machine learning,contactless | Template matching,Computer vision,Smart applications,Software deployment,Latency (engineering),Gesture,Computer science,Gesture recognition,Artificial intelligence,Radio-frequency identification,Distributed computing,Wavelet | Conference |
ISSN | ISBN | Citations |
2155-5486 | 978-1-7281-1208-4 | 1 |
PageRank | References | Authors |
0.35 | 23 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shigeng Zhang | 1 | 539 | 50.80 |
| Chengwei Yang | 2 | 11 | 2.80 |
| Xiaoyan Kui | 3 | 14 | 1.91 |
| Jianxin Wang | 4 | 8 | 6.73 |
| Xuan Liu | 5 | 297 | 38.07 |
| Song Guo | 6 | 3431 | 278.71 |