RadarNet: Efficient Gesture Recognition Technique Utilizing a Miniature Radar Sensor
| Autor: | Lauren Bedal, Eiji Hayashi, Leonardo Giusti, Nicholas Gillian, Jin Yamanaka, Jaime Lien, Ivan Poupyrev, Dave Weber |
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| Rok vydání: | 2021 |
| Předmět: |
Ambient intelligence
Modality (human–computer interaction) business.industry Computer science 05 social sciences 020207 software engineering 02 engineering and technology Convolutional neural network law.invention Radar engineering details Gesture recognition law Robustness (computer science) 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences Computer vision Artificial intelligence Radar Image sensor business 050107 human factors |
| Zdroj: | CHI |
| DOI: | 10.1145/3411764.3445367 |
| Popis: | Gestures 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. |
| Databáze: | OpenAIRE |
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