Interacting with Soli: Exploring Fine-Grained Dynamic Gesture Recognition in the Radio-Frequency Spectrum
By Saiwen Wang, Jie Song, Jaime Lien, Ivan Poupyrev, Otmar Hilliges. ACM Symposium on User Interface Software and Technology, 2016
426 citations recorded by the source corpus.
This paper proposes a novel machine learning architecture, specifically designed for radio-frequency based gesture recognition. We focus on high-frequency (60]GHz), short-range radar based sensing, in particular Google's Soli sensor. The signal has unique properties such as resolving motion at a very fine level and allowing for segmentation in range and velocity spaces rather than image space. This enables recognition of new types of inputs but poses significant difficulties for the design of input recognition algorithms. The proposed algorithm is capable of detecting a rich set of dynamic gestures and can resolve small motions of fingers in fine detail. Our technique is based on an end-to-end trained combination of deep convolutional and recurrent neural networks. The algorithm achieves high recognition rates (avg 87%) on a challenging set of 11 dynamic gestures and generalizes well across 10 users. The proposed model runs on commodity hardware at 140 Hz (CPU only).
Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/a2e3642a1ab591b67afbeee242bc7118b7b3e50f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
In Electrical and Electronic Engineering
- Paul Patras
- Moeness G. Amin
- Hamed Haddadi
- George Chryssolouris
- Chaoyun Zhang
- Erik G. Larsson
- Meysam Sadeghi
Citations in the corpus
- Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “[229] Gesture recognition Edge-based CNN, RNN Gao et al.”
- Radar-Based Human-Motion Recognition With Deep Learning: Promising applications for indoor monitoring (2019, methodology): “In gesture recognition, a combination of 3D inputs has been used with LSTM RNNs for classification [15], [19], but results were only presented for data samples containing a single motion class.”
- Symbiotic human-robot collaborative assembly (2019, background): “It allows a human hand to become a natural, intuitive interface for a robot [255].”