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

• URL: http://arxiv.org/abs/2008.01148v1
• Date: Mon, 3 Aug 2020 19:34:48 GMT
• Title: HAMLET: A Hierarchical Multimodal Attention-based Human Activity Recognition Algorithm
• Authors: Md Mofijul Islam and Tariq Iqbal
• Abstract summary: Human activity recognition (HAR) is a challenging task for robots due to difficulties related to multimodal data fusion. In this work, we introduce a neural network-based multimodal algorithm, HAMLET. We develop a novel multimodal attention mechanism for disentangling and fusing the salient unimodal features to compute the multimodal features in the upper layer.
• Score: 5.276937617129594
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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