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A survey of attention mechanisms for wearable sensor-based human activity recognition

Authors: Wang, X., Wang, Y., Geng, Y., Yu, H., Yang, H., Wen, X. and Wang, A.

Journal: CEUR Workshop Proceedings

Volume: 3459

Pages: 25-36

ISSN: 1613-0073

Abstract:

Attention mechanisms, widely used in many fields such as computer vision (CV) and natural language processing (NLP), enable deep learning networks to extract more important information from the input, thereby improving performance and efficiency. Recently, attention mechanisms are introduced to wearable sensor-based human activity recognition (WSHAR) for learning more robust feature representations. This paper investigates the attention mechanisms in WSHAR with a special focus on the principles of computing attention and the targets on which the attention works in a network and future directions. The aim is to provide readers with a clearer understanding of attention mechanisms in WSAHR and motivate more diverse work in the future.

Source: Scopus