Cross-Domain Activity Recognition Based on Stacked Transfer Network

Human activity recognition (HAR) based on wearable sensors is a hot topic in health detection and motion management. Nonetheless, conventional identification methods necessitate substantial labeled datasets, and the acquisition of high-quality labeled data crucial for human activity recognition is both time-consuming and costly. To tackle this problem, transfer learning is used to annotating unlabeled or a few labeled target domains using labeled source domains. Meanwhile, individuals exhibit significant differences in amplitude, angles, and other aspects when performing the same action. Due to the limited expressive capacity of time series, effectively capturing various types of differences simultaneously poses a challenge, impacting the effectiveness of transfer. Therefore, in this paper, we propose a stacked transfer network (STN) for 2D modeling and feature extraction of sensor data in steps. It adaptively decomposes complex temporal variations into multiple intra- and inter-periodic variations using the Fast Fourier Transform (FFT), allowing the model to focus more on the trends of the activty and less on the effects of individual discrepancy. Our comprehensive cross-domain activity recognition (CDAR) experiments on three large public activity recognition datasets (i.e., OPPORTUNITY, PAMAP2, and UCI-DSADS) show that the STN achieves high accuracy in activity recognition ttransfer.