Human Activity Recognition Using IR-UWB Radar: A Lightweight Transformer Approach

In this study, we introduce MobileViTX, an enhanced MobileViT architecture for human activity recognition (HAR) in impulse radio ultrawideband (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 a squeeze-and-excitation (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 fivefold cross-validation to assess our model's performance. Results show that MobileViTX outperforms the original MobileViT and other models in both recognition rate and efficiency.