Smart Glove and Hand Gesture-based Control Interface For Multi-rotor Aerial Vehicles
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".