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Learning multi-level features for sensor-based human action recognition
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DOI：10.1016/j.pmcj.2017.07.001.png)
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Abstract
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This paper proposes a multi-level feature learning framework for human action recognition using a single body-worn inertial sensor. The framework consists of three phases, respectively designed to analyze signal-based (low-level), components (mid-level) and semantic (high-level) information. Low-level features capture the time and frequency domain property while mid-level representations learn the composition of the action. The Max-margin Latent Pattern Learning (MLPL) method is proposed to learn high-level semantic descriptions of latent action patterns as the output of our framework. The proposed method achieves the state-of-the-art performances, 88.7%, 98.8% and 72.6% (weighted F-1 score) respectively, on Skoda, WISDM and OPP datasets. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Multi-level
Human action recognition
Latent pattern
High-level
Semantic
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