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

• Published in: Mathematics (Basel) Vol. 10; no. 24; p. 4753
• Main Authors: Jeon, Haneul, Choi, Haegyeom, Noh, Donghyeon, Kim, Taeho, Lee, Donghun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2022
• Subjects: Accuracy; Algorithms; Alignment; bi-directional LSTM; biomechanics; Calibration; Design and construction; Discriminant analysis; Floating bodies; Gesture recognition; hand-guiding gesture; Inertial guidance; Inertial navigation systems; Inertial sensing devices; Mathematical research; Methods; Remote control; Robust statistics; Robustness; Sensors; Sign language; Velocity; wearable sensor; Wearable technology; South Korea
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math10244753

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.