Name
Abstract | ||
|---|---|---|
This paper presents a recognition scheme for fine-grain gestures. The scheme leverages directional antenna and short-range wireless propagation properties to recognize a vocabulary of action-oriented gestures from the American Sign Language. Since the scheme only relies on commonly available wireless features such as Received Signal Strength (RSS), signal phase differences, and frequency subband selection, it is readily deployable on commercial-off-the-shelf IEEE 802.11 devices. We have implemented the proposed scheme and evaluated it in two potential application scenarios: gesture-based electronic activation from wheelchair and gesture-based control of car infotainment system. The results show that the proposed scheme can correctly identify and classify up to 25 fine-grain gestures with an average accuracy of 92% for the first application scenario and 84% for the second scenario. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2632048.2632095 | UbiComp |
Keywords | Field | DocType |
gestures recognition,user interfaces,wireless | Wheelchair,Wireless,Computer science,Gesture,Gesture recognition,Speech recognition,American Sign Language,Directional antenna,RSS,Vocabulary | Conference |
Citations | PageRank | References |
41 | 1.40 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro Melgarejo | 1 | 41 | 1.40 |
| Xinyu Zhang | 2 | 1343 | 78.62 |
| Parameswaran Ramanathan | 3 | 2794 | 271.11 |
| David Chu | 4 | 43 | 2.79 |