  We propose cross-modal attentive connections, a new dynamic and effective
technique for multimodal representation learning from wearable data. Our
solution can be integrated into any stage of the pipeline, i.e., after any
convolutional layer or block, to create intermediate connections between
individual streams responsible for processing each modality. Additionally, our
method benefits from two properties. First, it can share information
uni-directionally (from one modality to the other) or bi-directionally. Second,
it can be integrated into multiple stages at the same time to further allow
network gradients to be exchanged in several touch-points. We perform extensive
experiments on three public multimodal wearable datasets, WESAD, SWELL-KW, and
CASE, and demonstrate that our method can effectively regulate and share
information between different modalities to learn better representations. Our
experiments further demonstrate that once integrated into simple CNN-based
multimodal solutions (2, 3, or 4 modalities), our method can result in superior
or competitive performance to state-of-the-art and outperform a variety of
baseline uni-modal and classical multimodal methods.
