Name
Abstract | ||
|---|---|---|
ABSTRACTGestures are a promising candidate as an input modality for ambient computing where conventional input modalities such as touchscreens are not available. Existing works have focused on gesture recognition using image sensors. However, their cost, high battery consumption, and privacy concerns made cameras challenging as an always-on solution. This paper introduces an efficient gesture recognition technique using a miniaturized 60 GHz radar sensor. The technique recognizes four directional swipes and an omni-swipe using a radar chip (6.5 × 5.0 mm) integrated into a mobile phone. We developed a convolutional neural network model efficient enough for battery powered and computationally constrained processors. Its model size and inference time is less than 1/5000 compared to an existing gesture recognition technique using radar. Our evaluations with large scale datasets consisting of 558,000 gesture samples and 3,920,000 negative samples demonstrated our algorithm’s efficiency, robustness, and readiness to be deployed outside of research laboratories. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3411764.3445367 | Conference on Human Factors in Computing Systems |
Keywords | DocType | Citations |
gesture recognition, mobile, deep learning, radar sensing | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eiji Hayashi | 1 | 6 | 3.50 |
| Jaime Lien | 2 | 0 | 0.68 |
| Nicholas Gillian | 3 | 99 | 6.75 |
| Leonardo Giusti | 4 | 0 | 0.34 |
| Dave Weber | 5 | 1 | 1.04 |
| Jin Yamanaka | 6 | 0 | 0.34 |
| Lauren Bedal | 7 | 0 | 0.34 |
| Ivan Poupyrev | 8 | 3373 | 252.62 |