4.6 Article

A Hierarchical Learning Approach for Human Action Recognition

期刊

SENSORS
卷 20, 期 17, 页码 -

出版社

MDPI
DOI: 10.3390/s20174946

关键词

human action recognition; HAR; explainable deep-learning; 1D CNN; one-vs; -all; high-level activity recognition; intelligent sensors; modular method; light-weight method

资金

  1. Natural Sciences and Engineering Research Council of Canada
  2. Aerosystems International Inc.
  3. Prompt Quebec

向作者/读者索取更多资源

In the domain of human action recognition, existing works mainly focus on using RGB, depth, skeleton and infrared data for analysis. While these methods have the benefit of being non-invasive, they can only be used within limited setups, are prone to issues such as occlusion and often need substantial computational resources. In this work, we address human action recognition through inertial sensor signals, which have a vast quantity of practical applications in fields such as sports analysis and human-machine interfaces. For that purpose, we propose a new learning framework built around a 1D-CNN architecture, which we validated by achieving very competitive results on the publicly available UTD-MHAD dataset. Moreover, the proposed method provides some answers to two of the greatest challenges currently faced by action recognition algorithms, which are (1) the recognition of high-level activities and (2) the reduction of their computational cost in order to make them accessible to embedded devices. Finally, this paper also investigates the tractability of the features throughout the proposed framework, both in time and duration, as we believe it could play an important role in future works in order to make the solution more intelligible, hardware-friendly and accurate.

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