DanHAR: Dual Attention Network for multimodal human activity recognition using wearable sensors

In the paper, we present a new dual attention method called DanHAR, which blends channel and temporal attention on residual networks to improve feature representation ability for sensor-based HAR task. Specially, the channel attention plays a key role in deciding what to focus, i.e., sensor modalities, while the temporal attention can focus on the target activity from a long sensor sequence to tell where to focus. Extensive experiments are conducted on four public HAR datasets, as well as weakly labeled HAR dataset. The results show that dual attention mechanism is of central importance for many activity recognition tasks. We obtain 2.02%, 4.20%, 1.95%, 5.22% and 5.00% relative improvement over regular ConvNets respectively on WISDM dataset, UNIMIB SHAR dataset, PAMAP2 dataset, OPPORTUNITY dataset, as well as weakly labeled HAR dataset. The DanHAR is able to surpass other state-of-the-art algorithms at negligible computational overhead. Visualizing analysis is conducted to show that the proposed attention can capture the spatial-temporal dependencies of multimodal sensing data, which amplifies the more important sensor modalities and timesteps during classification. The results are in good agreement with normal human intuition. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The paper introduces a new dual attention method called DanHAR, which combines channel and temporal attention on residual networks to enhance feature representation for sensor-based HAR tasks. Experimental results on multiple datasets demonstrate the central importance of dual attention mechanism in activity recognition tasks, showing performance improvement over other algorithms. Visualizing analysis confirms the ability of the proposed attention mechanism to capture spatial-temporal dependencies in multimodal sensing data.