Depression significantly impacts mental health, severely disrupting patients' daily lives. During depressive episodes, individuals may experience symptoms such as excessive guilt, self-harm, and suicidal ideation. Compared to proprietary devices like brain electrode caps, wearable technologies for depression detection have gained attention due to their affordability and portability—enabling real-time monitoring of depressive states. However, challenges such as low-quality data from ubiquitous devices, individual variability, and the complexity of multimodal physiological signal analysis limit model generalizability. To address these issues, we present DepGuard, a novel ubiquitous wearable system for depression assessment based on multimodal physiological signals. DepGuard performs a two-stage detection process: depression recognition and real-time episode monitoring. For depression recognition, we propose an unsupervised domain adaptation method to reduce the domain gap between source and target subjects. For episode monitoring, we employ a few-shot learning strategy to enable personalized modeling. Both approaches enhance cross-subject generalization. Our system achieves 90.75% accuracy in cross-subject depression recognition using 30 unlabeled samples per target subject, and 93.52% accuracy in episode monitoring using 15 labeled samples per class.