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.

This study proposes a telemanipulation framework with two wearable IMU sensors without human skeletal kinematics. First, the states (intensity and direction) of spatial hand-guiding gestures are separately estimated through the proposed state estimator, and the states are also combined with the gesture’s mode (linear, angular, and via) obtained with the bi-directional LSTM-based mode classifier. The spatial pose of the 6-DOF manipulator’s end-effector (EEF) can be controlled by combining the spatial linear and angular motions based on integrating the gesture’s mode and state. To validate the significance of the proposed method, the teleoperation of the EEF to the designated target poses was conducted in the motion-capture space. As a result, it was confirmed that the mode could be classified with 84.5% accuracy in real time, even during the operator’s dynamic movement; the direction could be estimated with an error of less than 1 degree; and the intensity could be successfully estimated with the gesture speed estimator and finely tuned with the scaling factor. Finally, it was confirmed that a subject could place the EEF within the average range of 83 mm and 2.56 degrees in the target pose with only less than ten consecutive hand-guiding gestures and visual inspection in the first trial.