SCLAiR : Supervised Contrastive Learning for User and Device Independent Airwriting Recognition

TL;DR

This paper introduces SCLAiR, a supervised contrastive learning approach for airwriting recognition that is robust across different users and devices, improving accuracy in gesture-based human-computer interaction.

Contribution

It proposes a novel two-stage training strategy using supervised contrastive learning for device and user-independent airwriting recognition, with extensive experiments validating its effectiveness.

Findings

Outperforms existing domain adaptation techniques.

Effective in both supervised and unsupervised settings.

Demonstrates robustness across different devices and users.

Abstract

Airwriting Recognition is the problem of identifying letters written in free space with finger movement. It is essentially a specialized case of gesture recognition, wherein the vocabulary of gestures corresponds to letters as in a particular language. With the wide adoption of smart wearables in the general population, airwriting recognition using motion sensors from a smart-band can be used as a medium of user input for applications in Human-Computer Interaction. There has been limited work in the recognition of in-air trajectories using motion sensors, and the performance of the techniques in the case when the device used to record signals is changed has not been explored hitherto. Motivated by these, a new paradigm for device and user-independent airwriting recognition based on supervised contrastive learning is proposed. A two stage classification strategy is employed, the first of which involves training an encoder network with supervised contrastive loss. In the subsequent stage, a classification head is trained with the encoder weights kept frozen. The efficacy of the proposed method is demonstrated through experiments on a publicly available dataset and also with a dataset recorded in our lab using a different device. Experiments have been performed in both supervised and unsupervised settings and compared against several state-of-the-art domain adaptation techniques. Data and the code for our implementation will be made available at https://github.com/ayushayt/SCLAiR.