A novel rehabilitation action recognition approach using AF-LiteFormer from millimeter wave radar

Abstract Rehabilitation action recognition is a hot research topic in the medical field, which serves as the foundation for achieving remote healthcare, unsupervised exercise, intelligent home healthcare, and possesses extensive application value. Currently, vision-based action recognition methods are susceptible to limitations imposed by factors such as range of motion and environmental lighting during human motion capture. Due to its ability to effectively protect patient privacy and its immunity to lighting conditions, this paper proposed a millimeter-wave radar-based rehabilitation action recognition system, AF-LiteFormer. Firstly, EfficientFormerV2 is employed as the baseline, then a Lite-MSLA-FFN block-and-layer is designed to replace MHSA in EfficientFormerV2, which improves the diversity of attention and reduces computational complexity. Meanwhile, a Lite-Subsample block-and-layer is designed to replace the dual-path downsampling part that composed of attention downsampling and stride attention to realize global modeling and multi-scale learning. Secondly, the iterative attention feature fusion mechanism is introduced to learn rich feature representation for improving the recognition accuracy of rehabilitation actions. Finally, the effectiveness of the AF-LiteFormer model is validated on a self-collected rehabilitation action dataset and a publicly available micro-Doppler dataset. Experimental results show that the overall performance of the AF-LiteFormer model is better than the state-of-the-art models, the recognition accuracy of rehabilitation actions is as high as 99.7%, and it has strong generalization ability.