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Analysis and Recognition of Human Gait Activity Based on Multimodal Sensors

Author

Listed:
  • Diego Teran-Pineda

    (Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur, 35, 29071 Málaga, Spain)

  • Karl Thurnhofer-Hemsi

    (Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur, 35, 29071 Málaga, Spain
    Biomedical Research Institute of Málaga (IBIMA), C/ Doctor Miguel Díaz Recio, 28, 29010 Málaga, Spain)

  • Enrique Dominguez

    (Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur, 35, 29071 Málaga, Spain)

Abstract

Remote health monitoring plays a significant role in research areas related to medicine, neurology, rehabilitation, and robotic systems. These applications include Human Activity Recognition (HAR) using wearable sensors, signal processing, mathematical methods, and machine learning to improve the accuracy of remote health monitoring systems. To improve the detection and accuracy of human activity recognition, we create a novel method to reduce the complexities of extracting features using the HuGaDB dataset. Our model extracts power spectra; due to the high dimensionality of features, sliding windows techniques are used to determine frequency bandwidth automatically, where an improved QRS algorithm selects the first dominant spectrum amplitude. In addition, the bandwidth algorithm has been used to reduce the dimensionality of data, remove redundant dimensions, and improve feature extraction. In this work, we have considered widely used machine learning classifiers. Our proposed method was evaluated using the accelerometer angles spectrum installed in six parts of the body and then reducing the bandwidth to know the evolution. Our approach attains an accuracy rate of 95.1% in the HuGaDB dataset with 70% of bandwidth, outperforming others in the human activity recognition accuracy.

Suggested Citation

  • Diego Teran-Pineda & Karl Thurnhofer-Hemsi & Enrique Dominguez, 2023. "Analysis and Recognition of Human Gait Activity Based on Multimodal Sensors," Mathematics, MDPI, vol. 11(6), pages 1-17, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1538-:d:1103868
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    References listed on IDEAS

    as
    1. Jong-Yih Kuo & Shang-Yi You & Hui-Chi Lin & Chao-Yang Hsu & Baiying Lei, 2022. "Constructing Condition Monitoring Model of Wind Turbine Blades," Mathematics, MDPI, vol. 10(6), pages 1-13, March.
    2. Francisco Beltran-Carbajal & Hugo Francisco Abundis-Fong & Luis Gerardo Trujillo-Franco & Hugo Yañez-Badillo & Antonio Favela-Contreras & Eduardo Campos-Mercado, 2022. "Online Frequency Estimation on a Building-like Structure Using a Nonlinear Flexible Dynamic Vibration Absorber," Mathematics, MDPI, vol. 10(5), pages 1-14, February.
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