MDSCNet: A Lightweight Radar Image-Based Model for Multi-Action Classification in Elderly Healthcare

This study presents MDSCNet, a compact radar image-based deep learning model for multi-action classification in elderly healthcare scenarios. Motivated by the need for real-time deployment on resource-constrained devices, MDSCNet employs a streamlined architecture with a small number of lightweight expansion–depthwise–projection blocks, removing complex attention and squeeze-and-excitation modules to minimize computational overhead. The model is evaluated on a millimeter-wave radar dataset covering five healthcare-related actions: lying, sitting, standing, bed-exit, and falling, performed by 15 participants on an actual electric nursing bed. The experimental results demonstrate that MDSCNet achieves accuracy comparable to state-of-the-art CNN-based methods while maintaining an extremely compact model size of only 0.29 MB, showing its suitability for practical elderly care applications where both accuracy and efficiency are critical.