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

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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.
Funding Information
  • National Research Foundation of Korea (NRF-2022 R1F1A1074704)
  • Ministry of Science and ICT, South Korea (IITP-2022-RS-2022-00156360)
  • Institute of Information & Communications Technology Planning & Evaluation (No.2022-0-00218)
  • Korea Institute for Advancement of Technology (N000P0017033)

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