Wearable Inertial Sensor-Based Hand-Guiding Gestures Recognition Method Robust to Significant Changes in the Body-Alignment of Subjectopen access
- Authors
- Jeon, Haneul; Choi, Haegyeom; Noh, Donghyeon; Kim, Taeho; Lee, Donghun
- Issue Date
- Dec-2022
- Publisher
- MDPI
- Keywords
- gesture recognition; bi-directional LSTM; wearable sensor; biomechanics; hand-guiding gesture
- Citation
- MATHEMATICS, v.10, no.24
- Journal Title
- MATHEMATICS
- Volume
- 10
- Number
- 24
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/43345
- DOI
- 10.3390/math10244753
- ISSN
- 2227-7390
- 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.
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