High Accuracy Human Activity Recognition Based on Sparse Locality Preserving Projections

Xiangbin Zhu; Huiling Qiu

doi:10.1371/journal.pone.0166567

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Open Access

Peer-reviewed

Research Article

High Accuracy Human Activity Recognition Based on Sparse Locality Preserving Projections

Xiangbin Zhu ,

Contributed equally to this work with: Xiangbin Zhu, Huiling Qiu

* E-mail: zhuxb@zjnu.cn

Affiliation College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Zhejiang, China
⨯
Huiling Qiu

Contributed equally to this work with: Xiangbin Zhu, Huiling Qiu

Affiliation College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Zhejiang, China
⨯

High Accuracy Human Activity Recognition Based on Sparse Locality Preserving Projections

Xiangbin Zhu,
Huiling Qiu

Published: November 28, 2016
https://doi.org/10.1371/journal.pone.0166567

About the Authors

Xiangbin Zhu

Contributed equally to this work with: Xiangbin Zhu, Huiling Qiu

* E-mail: zhuxb@zjnu.cn

Affiliation College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Zhejiang, China

Huiling Qiu

Contributed equally to this work with: Xiangbin Zhu, Huiling Qiu

Affiliation College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Zhejiang, China

Competing Interests

The authors have declared that no competing interests exist.

Author Contributions

Conceptualization: XZ. Data curation: HQ. Formal analysis: XZ HQ. Methodology: XZ HQ. Project administration: XZ. Resources: XZ. Software: HQ. Supervision: XZ. Visualization: HQ. Writing – original draft: HQ XZ.

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Hidden Markov models
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Cell phones
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Sensory perception
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Algorithms
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Markov models
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Accelerometers
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Probability distribution
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Neighborhoods
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