Implementation of real-time motion and force capturing system for tele-manipulation based on sEMG signals and IMU motion data

Kim, Min Kyu; Ryu, Kwanghyun; Oh, Yonghwan; Oh, Sang-Rok; Kim, Kee Hoon

doi:10.1109/icra.2014.6907691

Cited by 14 publications

(1 citation statement)

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“…In Ref [ 26 ], Mardiyanto, R. proposed to control the underwater remote-operated vehicle by mounting the IMU to the elbow, forearm, and wrist, and to control the gripper of the robot by attaching the flex sensor to the finger. In Ref [ 27 ], Kim, M.K. presented a real-time motion and force capturing system that combines sEMG with IMU.…”

Section: Previous Researchmentioning

confidence: 99%

Motion Estimation and Hand Gesture Recognition-Based Human–UAV Interaction Approach in Real Time

Yoo

Song

et al. 2022

Sensors

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As an alternative to traditional remote controller, research on vision-based hand gesture recognition is being actively conducted in the field of interaction between human and unmanned aerial vehicle (UAV). However, vision-based gesture system has a challenging problem in recognizing the motion of dynamic gesture because it is difficult to estimate the pose of multi-dimensional hand gestures in 2D images. This leads to complex algorithms, including tracking in addition to detection, to recognize dynamic gestures, but they are not suitable for human–UAV interaction (HUI) systems that require safe design with high real-time performance. Therefore, in this paper, we propose a hybrid hand gesture system that combines an inertial measurement unit (IMU)-based motion capture system and a vision-based gesture system to increase real-time performance. First, IMU-based commands and vision-based commands are divided according to whether drone operation commands are continuously input. Second, IMU-based control commands are intuitively mapped to allow the UAV to move in the same direction by utilizing estimated orientation sensed by a thumb-mounted micro-IMU, and vision-based control commands are mapped with hand’s appearance through real-time object detection. The proposed system is verified in a simulation environment through efficiency evaluation with dynamic gestures of the existing vision-based system in addition to usability comparison with traditional joystick controller conducted for applicants with no experience in manipulation. As a result, it proves that it is a safer and more intuitive HUI design with a 0.089 ms processing speed and average lap time that takes about 19 s less than the joystick controller. In other words, it shows that it is viable as an alternative to existing HUI.

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Section: Previous Researchmentioning

confidence: 99%