CSI-Based Cross-Domain Activity Recognition via Zero-Shot Prototypical Networks

TL;DR

This paper introduces a zero-shot prototype recurrent convolutional network that enhances cross-domain human activity recognition using CSI data, addressing the challenge of labeling new domain samples and improving transferability.

Contribution

It proposes a novel zero-shot learning approach with prototype networks for CSI-based HAR, enabling classification of unseen activities across domains.

Findings

Improves cross-domain HAR accuracy over existing methods.

Effective in classifying unseen activities with unlabeled target data.

Validated on three real-world datasets with positive results.

Abstract

The cross-domain capability of wireless sensing is currently one of the major challenges on human activity recognition (HAR) based on the channel state information (CSI) of wireless signals. The difficulty of labeling samples from new domains has encouraged the use of few and zero shot strategies. In this context, prototype networks have attracted attention due to their reasonable cross-domain transferability. This paper presents a novel zero-shot prototype recurrent convolutional network that implements a zero-shot learning strategy for HAR via CSI. This method extracts the prototypes from an available source domain to classify unseen and unlabeled data from the target domain for the same or similar classes. The experiments have been developed using three datasets with real measurements, and the results include an inter-datasets evaluation. Overall, the results improve the state of the art and make it a promising solution for cross-domain HAR.