Teaching a Robot Sign Language using Vision-Based Hand Gesture Recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a novel vision-based hand gesture recognition (HGR) and training system for a human-like robot hand. We implemented and trained a multiclass-SVM classifier and N-Dimensional DTW (ND-DTW) classifier for static posture recognition and dynamic gesture recognition. Training features were extracted from the raw gestures depth data captured by Leap Motion Controller. The experimental results show that multiclass SVM method has an average 98.25% recognition rates and the shortest run time when compared to k-NN and ANBC. For dynamic gestures, ND-DTW classifier displays a better performance than DHMM with an average 95.5% recognition rate and significantly shorter run time. In conclusion, the combination of SVMs and DTW proves the efficiency and high accuracy in proposed human-robot interaction system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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