  To fluently collaborate with people, robots need the ability to recognize
human activities accurately. Although modern robots are equipped with various
sensors, robust human activity recognition (HAR) still remains a challenging
task for robots due to difficulties related to multimodal data fusion. To
address these challenges, in this work, we introduce a deep neural
network-based multimodal HAR algorithm, HAMLET. HAMLET incorporates a
hierarchical architecture, where the lower layer encodes spatio-temporal
features from unimodal data by adopting a multi-head self-attention mechanism.
We develop a novel multimodal attention mechanism for disentangling and fusing
the salient unimodal features to compute the multimodal features in the upper
layer. Finally, multimodal features are used in a fully connect neural-network
to recognize human activities. We evaluated our algorithm by comparing its
performance to several state-of-the-art activity recognition algorithms on
three human activity datasets. The results suggest that HAMLET outperformed all
other evaluated baselines across all datasets and metrics tested, with the
highest top-1 accuracy of 95.12% and 97.45% on the UTD-MHAD [1] and the
UT-Kinect [2] datasets respectively, and F1-score of 81.52% on the UCSD-MIT [3]
dataset. We further visualize the unimodal and multimodal attention maps, which
provide us with a tool to interpret the impact of attention mechanisms
concerning HAR.
