Human Activity Recognition Using IR-UWB Radar: A Lightweight Transformer Approach
Xiaoxiong Li, Si Chen, Shuning Zhang, Linsheng Hou, Yuying Zhu, Zelong Xiao
Abstract
In this study, we introduce MobileViTX, an enhanced MobileViT architecture for human activity recognition in impulse radio ultra-wideband (IR-UWB) radar applications. MobileViT is a lightweight Vision Transformer mainly consisting of MobileViT blocks and MobileNetv2 blocks. Modifications to the MobileNetv2 block include adding a Drop Path and an SE module and altering activation functions to hard-sigmoid and hard-swish. Additionally, the self-attention in the MobileViT block is transformed to possess linear complexity. These adjustments aim to accelerate inference while preserving high accuracy. We experiment with a dataset from 20 individuals performing 20 distinct actions, using 5-fold cross-validation to assess our model’s performance. Results show MobileViTX outperforms the original MobileViT and other models in both recognition rate and efficiency.