HAMLET: A Hierarchical Multimodal Attention-based Human Activity Recognition Algorithm

TL;DR

HAMLET is a deep neural network that uses hierarchical multimodal attention to improve human activity recognition accuracy across multiple datasets, aiding robots in better understanding human actions.

Contribution

This work introduces HAMLET, a novel hierarchical multimodal attention-based neural network for human activity recognition, effectively fusing multimodal data with improved accuracy.

Findings

HAMLET outperformed state-of-the-art algorithms on three datasets.

Highest accuracy of 95.12% and 97.45% on UTD-MHAD and UT-Kinect.

F1-score of 81.52% on UCSD-MIT.

Abstract

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.