In the field of Human Activity Recognition (HAR), the rapid evolution of wearable devices necessitates models that are generalizable and can adapt to entirely new subjects and activities with very limited labeled data. Conventional deep learning models, constrained by their reliance on large training datasets and limited adaptability to novel scenarios, face challenges in these settings. This paper introduces a novel few-shot HAR strategy employing meta-learning, which facilitates rapid adaptation to unseen subjects and activities using minimal annotated samples. Our method augments time series data with a range of transformations, each assigned a learnable weight, enabling the model to prioritize the most effective augmentations and discard the irrelevant ones. Throughout the meta-training phase, the model learns to identify an optimally weighted combination of these transformations, significantly improving the model’s adaptability and generalization to new situations with scarce labeled data. During meta-testing, this knowledge enables the model to efficiently learn from and adapt to a very limited set of labeled samples from completely new subjects undertaking entirely new activities. Extensive experiments on various HAR datasets demonstrate our method’s enhanced adaptability and generalization to tasks never encountered during training, achieving a performance improvement of up to  across all datasets. These results affirm MADA’s potential for real-world applications characterized by limited data availability. The Author(s) 2025