Cross-Domain Human Activity Recognition via Domain Adaptation and Fused Attention

In recent years, the utilization of wearable sensors for Human Activity Recognition (HAR) has garnered significant interest in the fields of medical health monitoring and sports management. However, HAR often suffer the poor generalization from the insufficient labeled data for complex activities. To address this issue, the novel Transfer Component Analysis-Bidirectional Long Short-Term Memory network (TCA-BiLSTM) with the fused attention mechanism is presented in this paper. Specifically, TCA-BiLSTM first leverages the Maximum Mean Difference (MMD) within the Reproducing Kernel Hilbert Space (RKHS) to learn transfer components for sensor-based HAR. These derived transfer components align the data collected from sensors deployed on different body parts, facilitating the mapping of cross-domain HAR data. Then, the two-layer BiLSTM with the novel fused attention mechanism is given to classify the unseen activities, which aims to capture the multi-granularity activity information after the TCA-based domain adaptation. To evaluate the effectiveness of TCA-BiLSTM, a series of experiments were conducted using the DSADS and PAMAP2 datasets. The results demonstrate that TCA-BiLSTM outperforms the state-of-art methods such as DSAN and FNet, achieving performance improvements of 6.1% and 2.5%, respectively.