Differentiable Prior-Driven Data Augmentation for Sensor-Based Human Activity Recognition

Abstract

Sensor-based human activity recognition (HAR) usually suffers from the problem of insufficient annotated data, due to the difficulty in labeling the intuitive signals of wearable sensors. To this end, recent advances have adopted handcrafted operations or generative models for data augmentation. The handcrafted operations are driven by some physical priors of human activities, e.g., action distortion and strength fluctuations. However, these approaches may face challenges in maintaining semantic data properties. Although the generative models have better data adaptability, it is difficult for them to incorporate important action priors into data generation. This article proposes a differentiable prior-driven data augmentation framework for HAR. First, we embed the handcrafted augmentation operations into a differentiable module, which adaptively selects and optimizes the operations to be combined together. Then, we construct a generative module to add controllable perturbations to the data derived by the handcrafted operations and further improve the diversity of data augmentation. By integrating the handcrafted operation module and the generative module into one learnable framework, the generalization performance of the recognition models is enhanced effectively. Extensive experimental results with three different classifiers on five public datasets demonstrate the effectiveness of the proposed framework. Project page: https://github.com/crocodilegogogo/DriveData-Under-Review.