OD-GCResNet: A Deep Learning Model for Kitchen Activity Recognition Using Micro-Doppler Signatures

As the global aging population grows, the need for non-invasive, reliable monitoring solutions for elderly individuals living alone becomes urgent. Kitchen activities, a highrisk area in homes, pose unique safety challenges. However, existing human activity recognition methods still struggle with accuracy, and few studies specifically address these challenges in kitchen environments. This paper introduces OD-GCResNet, a novel hybrid deep learning model for kitchen activity recognition based on micro-Doppler signatures. The proposed model combines Omni-Dimensional Dynamic Convolution with Recursive Gated Convolution to enhance global feature interactions and adaptive attention, allowing for accurate detection of subtle micro-Doppler variations in complex, real-world environments. We validate OD-GCResNet on our collected kitchen activity dataset and achieve a classification accuracy of 99.61 %, outperforming baseline models. This work represents a significant step forward in non-contact, privacy-preserving safety monitoring solutions for elderly care. Our dataset and codes are available at https://github.com/Canberra1111/Kitchen-Micro-Doppler-HAR-with-OD-GCResNet.