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Wrapper Filter Approach for Accelerometer-Based Human Activity Recognition

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Abstract

With the widespread use of mobile devices all over the world, a new interesting and challenging research area known as Activity Recognition (AR) with many application domains is evolved. Basically, activity recognition aims to identify certain physical activities such as walking, jogging, sitting, standing, etc., performed daily by humans. In this paper, we investigated the effectiveness of wrapper-based feature selection approach for accelerometer-based human activity recognition. Our approach utilizes Sequential Forward Selection (SFS) technique based on three machine learning algorithms: Random Forest (RF), K-Nearest Neighbor (K-NN), and Gradient-Boosted Tree (GBT). A standard and publicly available dataset called WISDM (Wireless Sensor Data Mining), which contains accelerometer-based time series data collected from thirty-six volunteers, was used for performance evaluation of the proposed model. The experimental results showed that our GBT-based recognition model outperforms previously suggested solutions and establishing state-of-the-art performance for this dataset.

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Funding

This work was funded by the authors equally.

Author information

Authors and Affiliations

  1. Middle Technical University, Baghdad, Iraq

    Laith Al-Frady & Ali Al-Taei

  2. Ministry of Higher Education and Scientific Research, Baghdad, Iraq

    Ali Al-Taei

Authors
  1. Laith Al-Frady
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  2. Ali Al-Taei
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Contributions

All authors contributed equally to this work.

Corresponding authors

Correspondence to Laith Al-Frady or Ali Al-Taei.

Ethics declarations

CONFLICT OF INTEREST

The authors declare that they have no conflict of interest.

CORRESPONDING AUTHOR

Correspondence to: Ali Al-Taei.

ETHICAL APPROVAL

This article does not contain any studies involving human participants or animals performed by any of the authors.

Additional information

Laith S. Al-Frady received his B.Sc. degree in Information Technology from the Middle Technical University, Iraq in 2009. He held M.Sc. degree in Information Technology from the same university in 2016. Currently, he works as lecturer at the Technical College of Administration, IT Department. His research interests are in the area of machine learning, data mining, and statistics.

Ali Al-Taei received the B.Sc. degree in Computer Science from the University of Baghdad, Iraq, and the Post Graduate Diploma degree in Artificial Intelligence from the University of Technology, Baghdad, Iraq, in 2002 and 2003, respectively. He held the M.Sc. degree in Computer Engineering from Çankaya University, Turkey, in 2015. Currently, he works at the Minister’s Office, Ministry of Higher Education and Scientific Research, Baghdad, Iraq, and lecturer at the IT Dept., Technical College of Management, Middle Technical University, Baghdad, Iraq. His main areas of research interest are machine learning, pattern recognition, and data mining.

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Al-Frady, L., Al-Taei, A. Wrapper Filter Approach for Accelerometer-Based Human Activity Recognition. Pattern Recognit. Image Anal. 30, 757–764 (2020). https://doi.org/10.1134/S1054661820040033

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  • DOI: https://doi.org/10.1134/S1054661820040033

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