  Human activity recognition (HAR) in ubiquitous computing has been beginning
to incorporate attention into the context of deep neural networks (DNNs), in
which the rich sensing data from multimodal sensors such as accelerometer and
gyroscope is used to infer human activities. Recently, two attention methods
are proposed via combining with Gated Recurrent Units (GRU) and Long Short-Term
Memory (LSTM) network, which can capture the dependencies of sensing signals in
both spatial and temporal domains simultaneously. However, recurrent networks
often have a weak feature representing power compared with convolutional neural
networks (CNNs). On the other hand, two attention, i.e., hard attention and
soft attention, are applied in temporal domains via combining with CNN, which
pay more attention to the target activity from a long sequence. However, they
can only tell where to focus and miss channel information, which plays an
important role in deciding what to focus. As a result, they fail to address the
spatial-temporal dependencies of multimodal sensing signals, compared with
attention-based GRU or LSTM. In the paper, we propose a novel dual attention
method called DanHAR, which introduces the framework of blending channel
attention and temporal attention on a CNN, demonstrating superiority in
improving the comprehensibility for multimodal HAR. Extensive experiments on
four public HAR datasets and weakly labeled dataset show that DanHAR achieves
state-of-the-art performance with negligible overhead of parameters.
Furthermore, visualizing analysis is provided to show that our attention can
amplifies more important sensor modalities and timesteps during classification,
which agrees well with human common intuition.
