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Wearable Inertial Sensor-Based Hand-Guiding Gestures Recognition Method Robust to Significant Changes in the Body-Alignment of Subject

Author

Listed:
  • Haneul Jeon

    (Mechanical Engineering Department, Soongsil University, Seoul 06978, Republic of Korea)

  • Haegyeom Choi

    (Mechanical Engineering Department, Soongsil University, Seoul 06978, Republic of Korea)

  • Donghyeon Noh

    (Mechanical Engineering Department, Soongsil University, Seoul 06978, Republic of Korea)

  • Taeho Kim

    (Mechanical Engineering Department, Soongsil University, Seoul 06978, Republic of Korea)

  • Donghun Lee

    (Mechanical Engineering Department, Soongsil University, Seoul 06978, Republic of Korea)

Abstract

The accuracy of the wearable inertia-measurement-unit (IMU)-sensor-based gesture recognition may be significantly affected by undesired changes in the body-fixed frame and the sensor-fixed frame according to the change in the subject and the sensor attachment. In this study, we proposed a novel wearable IMU-sensor-based hand-guiding gesture recognition method robust to significant changes in the subject’s body alignment based on the floating body-fixed frame method and the bi-directional long short-term memory (bi-LSTM). Through comparative experimental studies with the other two methods, it was confirmed that aligning the sensor-fixed frame with the reference frame of the human body and updating the reference frame according to the change in the subject’s body-heading direction helped improve the generalization performance of the gesture recognition model. As a result, the proposed floating body-fixed frame method showed a 91.7% test accuracy, confirming that it was appropriate for gesture recognition under significant changes in the subject’s body alignment during gestures.

Suggested Citation

  • Haneul Jeon & Haegyeom Choi & Donghyeon Noh & Taeho Kim & Donghun Lee, 2022. "Wearable Inertial Sensor-Based Hand-Guiding Gestures Recognition Method Robust to Significant Changes in the Body-Alignment of Subject," Mathematics, MDPI, vol. 10(24), pages 1-13, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4753-:d:1003236
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    Cited by:

    1. Haegyeom Choi & Haneul Jeon & Donghyeon Noh & Taeho Kim & Donghun Lee, 2023. "Hand-Guiding Gesture-Based Telemanipulation with the Gesture Mode Classification and State Estimation Using Wearable IMU Sensors," Mathematics, MDPI, vol. 11(16), pages 1-16, August.

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