Multi-agent Transformer Networks for Multimodal Human Activity Recognition

Human activity recognition has become an important challenge yet to resolve while also having promising benefits in various applications for years. Existing approaches have made great progress by applying deep learning and attention-based methods. However, the deep learning-based approaches may not fully exploit the features to resolve multimodal human activity recognition tasks. Also, the potential of attention-based methods still has not been fully explored to better extract the multimodal spatial-temporal relationship and produce robust results. In this work, we propose Multi-agent Transformer Network (MATN), a multi-agent attention-based deep learning algorithm, to address the above issues in multimodal human activity recognition. We first design a unified representation learning layer to encode the multimodal data, which preprocesses the data in a generalized and efficient way. Then we develop a multimodal spatial-temporal transformer module that applies the attention mechanism to extract the salient spatial-temporal features. Finally, we use a multi-agent training module to collaboratively select the informative modalities and predict the activity labels. We have extensively conducted experiments to evaluate MATN's performance on two public multimodal human activity recognition datasets. The results show that our model has achieved competitive performance compared to the state-of-the-art approaches, which also demonstrates scalability, effectiveness, and robustness.