UltraCLR: Contrastive Representation Learning Framework for Ultrasound-based Sensing

Abstract

We propose UltraCLR, a new contrastive learning framework that fuses dual modulation ultrasonic sensing signals to enhance gesture representation. Most existing ultrasound-based gesture recognition tasks rely on a large amount of manually labeled samples to learn task-specific representations via end-to-end training. However, they cannot exploit unlabeled continuous gesture signals that are easy to collect. Inspired by recent self-supervised learning techniques, UltraCLR aims to autonomously learn a ubiquitous gesture signal representation that can benefit all tasks from low-cost unlabeled signals. We use the STFT heatmap as a secondary input and leverage the contrastive learning framework to improve the high-quality Channel Impulsive Response (CIR) heatmap input representations. The learned representations can better represent the spatial-position information and intermediate states of gesture movement. With the representation learned by UltraCLR, we can greatly reduce the complexity of downstream gesture recognition tasks so that they can be completed using a simple classifier trained with a small training set and a lower computational cost. Our experimental results show that UltraCLR outperforms state-of-the-art gesture recognition systems with only a few labeled samples, and achieves more than 85% reduction in computational complexity and over 9x improvement in inference speed.