TFAugment: A Key Frequency-Driven Data Augmentation Method for Human Activity Recognition

Data augmentation enhances Human Activity Recognition (HAR) models by diversifying training data through transformations, improving their robustness. However, traditional techniques with random masking pose challenges by introducing randomness that can obscure critical information. This randomness may lead the model to learn incorrect patterns, yielding variable results across datasets and models and diminishing reliability and generalizability in real-world scenarios. To address this issue, this paper introduces Time-Frequency Augmentation (TFAugment), an adaptive method improving generalizability by selectively enhancing key frequencies across diverse datasets in HAR. The proposed method incorporates a FreqMasking module into the network to extract an importance distribution from incoming frequency channels. This distribution serves as a parameter in a Bernoulli distribution for independent sampling of each frequency channel, thereby generating enriched training data. Experiments on DSADS, MHEALTH, PAMAP2, and RealWorld-HAR datasets demonstrate TFAugment’s superior adaptability and significant performance enhancement compared to state-of-the-art techniques.