SCLAiR : Supervised Contrastive Learning for User and Device Independent Airwriting Recognition
2021
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.
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