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Hand Gesture Recognition and Biometric Authentication Using a Multi-day Dataset

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Intelligent Robotics and Applications (ICIRA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13458))

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Abstract

Hand-gesture recognition (HGR) is one of the major applications of electromyography (EMG), specifically for controlling functional prosthetic hands. Recently, another application i.e. the EMG-based biometrics has found growing research interest due to its potential of addressing some conventional biometric limitations. It has been observed that for EMG-based applications, the translation of laboratory research to real-life applications suffers from two major limitations: 1) a small subject pool, and 2) limited to single-session data recordings. In this study, forearm, and wrist EMG data were collected from 43 participants over three different days with long separation (Days 1, 8, and 29) while they performed static hand/wrist gestures. The HGR evaluation resulted in a mean AUC of 0.948 ± 0.018 and 0.941 ± 0.021 for forearm data and wrist data, respectively. The biometric evaluation resulted in a mean EER of 0.028 ± 0.007 and 0.038 ± 0.006 for forearm data and wrist data, respectively. These results were comparable to the widely used Ninapro database DB2. The large-sample multi-day dataset would facilitate further research on EMG-based HGR and biometric applications.

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Notes

  1. 1.

    https://dx.doi.org/10.21227/82v9-2b79.

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Authors and Affiliations

  1. National Clinical Research Center for Geriatrics, West China Hospital Sichuan University , Chengdu, Sichuan, China

    Jiayuan He & Ning Jiang

  2. Med-X Center for Manufacturing, Sichuan University, Chengdu, Sichuan, China

    Jiayuan He & Ning Jiang

  3. Engineering Bionics Lab, Department of Systems Design Engineering, University of Waterloo, Waterloo, N2L 3G1, Canada

    Ashirbad Pradhan

Authors
  1. Ashirbad Pradhan
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  2. Jiayuan He
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  3. Ning Jiang
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Corresponding author

Correspondence to Ning Jiang .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Honghai Liu

  2. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  3. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  4. Harbin Institute of Technology, Harbin, China

    Li Jiang

  5. Shanghai Jiao Tong University, Shanghai, China

    Guoying Gu

  6. Shenzhen Institute of Advanced Technology, Shenzhen, China

    Xinyu Wu

  7. Harbin Institute of Technology, Shenzhen, China

    Weihong Ren

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Pradhan, A., He, J., Jiang, N. (2022). Hand Gesture Recognition and Biometric Authentication Using a Multi-day Dataset. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13458. Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_35

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  • DOI: https://doi.org/10.1007/978-3-031-13841-6_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13840-9

  • Online ISBN: 978-3-031-13841-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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