Smart Glove and Hand Gesture-based Control Interface For Multi-rotor Aerial Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces an adaptable human-robot interface that uses two types of human-computer interactions: an image processing technique for a right-hand gesture recognition and a smart glove for left-hand commands. A fixed number of gestures is used for specific commands to the vehicle (takeoff, land, hover, etc.), while the smart glove is used for the vehicle motors control. A single shot multi-box detector (SSD) model is used for a hand detection. After removing the cluttered background, the region of interest (RoI) is fed to a convolutional neural network (CNN) for right-hand gesture recognition. We propose three concurrent validation layers including a human-based validation. The validation layers allow the system to adapt to various users including different skin colors and hand shapes. Four flex sensors and a motion processing unit (MPU) are used in the smart glove to measure the bending ratio of each finger and the roll angle of the left hand. These signals are used for a left-hand gesture recognition as well as generation of continuous control signals such as throttle and angle commands of the vehicle. Extensive experimental results are presented that validate the proposed control methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it