Data Augmentation for Human Activity Recognition via Condition Space Interpolation within a Generative Model

This paper presents a generative data augmentation approach for human activity recognition (HAR) to close the distribution gap between laboratory training and real-world deployment. Despite the recent success of deep learning methods in wearable sensor-based HAR tasks, performance degradation occurs during real-world deployment due to training data scarcity and the vast variability in human activities. In light of this, we aim to enhance the diversity of training datasets by generating new data points within the vicinity of existing samples, as informed by domain expertise. Unlike the commonly utilized methods that augment data by interpolating in data space or feature space, we innovate by applying interpolation in the condition space of a conditional generative model to augment HAR datasets. We use domain-specific knowledge to extract statistical metrics from sensor data, which serve as conditions to direct the generation process. We demonstrate how a conditional generative diffusion model, steered by interpolated conditions, can synthesize realistic new data with various high-level features that benefit the robustness of the downstream HAR models. Our methodology advances the use of interpolation in data augmentation by exploring the capability of a state-of-the-art generative model, offering novel perspectives for bolstering the robustness and generalizability of HAR systems. Experimental results demonstrate that condition space interpolation outperforms the conventional interpolation-based and generative model-based augmentation methods across various datasets and downstream classifier combinations.