On Domain Generalization for Human Activity Recognition with Mix-Based Methods

Domain generalization (DG) is a challenging problem that involves adapting a model trained on source domains to an unseen target domain.In human activity recognition (HAR), domain shifts often arise from differences in sensor placement, device specifications, or environmental factors, making generalization difficult.In this work, we investigate the effectiveness of mix-based methods like MixStyle and Exact Feature Distribution Mixing (EFDM) when integrated into state-of-the-art models like ResNet and TS2Vec for DG in HAR tasks, leveraging the DAGHAR benchmark.Our results demonstrate that MixStyle significantly outperforms both EFDM and Empirical Risk Minimization approaches, highlighting its effectiveness in addressing domain shifts.* This project was