An End-to-End Network for Continuous Human Motion Recognition via Radar Radios

Abstract

Micro-Doppler-based continuous human motion recognition (HMR) has gained considerable attention recently. However, existing methods mainly rely on individual recurrent neural network or sliding-window-based approaches, which makes them hard to effectively exploit all the temporal information to predict motions. Additionally, they need to represent the raw radar data into other domains and then perform feature extraction and classification. Thus, the representation cannot be optimized, and its high computational complexity and independence from learning model make the network consume significant time. In this article, to address these issues, we propose a new end-to-end network that uses radar radios to recognize continuous motion. Specifically, the fusion layer fuses the raw I & Q radar data without the need of representations, and it is integrated with subsequent networks in an end-to-end manner for jointly optimization. Moreover, the attention-based encoder-decoder structure encodes the fused data and selects useful temporal information for recognition, which guarantees the effective use of all the temporal information. The experiments show that in continuous HMR, the proposed network outperforms existing methods in terms of accuracy and inference time.