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Human activity recognition (HAR), the task of predicting the activities performed by an individual using mobile sensor data, is an active and important area of research. Unfortunately, it is very costly to collect the data required to train robust HAR classifiers. To tackle this issue, there has been an increasing focus on generating synthetic HAR data for augmentation purposes. The state-of-the-art generative HAR approaches utilize Generative Adversarial Networks (GANs) to produce realistic synthetic HAR data. However, these solutions can not generate personalized data that matches the behavior of particular users, limiting their potential use cases. This is particularly problematic in the mobile health domain, where the target users are often elderly or disabled and are thus likely to have activity signals that are unique from the general population. To overcome this drawback, we propose a novel controllable GAN …