Machine recognition of human gestures through principal joint variable analysis
Bibliographic record
Abstract
Gestures are a fundamental part of human communication and are becoming a key component of human-computer interaction. Traditionally, to teach computers to recognize specific gestures, researchers have used a sensor, usually a camera, to collect large gesture datasets, which are then classified and structured using machine learning techniques. Yet finding a way to confidently differentiate between several gesture classes has proven to be rather difficult for those working in the gesture recognition field. To capture the samples of movements necessary to train gesture recognition systems, the first step is to provide research participants with appropriate instructions. As collecting gesture data is the crucial first step of creating a robust gesture dataset, this dissertation will examine the modalities of instruction used in gesture recognition research to examine whether appropriate directives are conveyed to research participants. These experiments will result in the creation of a new dataset, the PJVA-20 dataset, comprised of 50 samples of 20 gesture classes sampled from 6 participants. After collecting the gesture samples of the PJVA-20 dataset, this dissertation will establish the benchmark recognition system PJVA — chiefly comprised of AMFE, Polynomial Motion Approximation, and Principal Component Analysis (PCA)—to contribute to the gesture recognition literature in terms of novel gesture recognition algorithms that can achieve high speed and accuracy results. This also involves examining studies in the gesture recognition literature to determine which machine learning algorithms offer reliability, speed, and accuracy for solving complex gesture recognition problems, as well as experimenting and testing the PJVA approach against other researchers in the Computer Vision and Machine Learning fields. In particular, the MSRC-12 research provides a benchmark point of comparison for research in this field. To test the quality of samples on the PJVA-20 against the MSRC-12, a new method is established for extracting motion feature vectors through a novel gesture recognition approach, AMFE. This is tested by applying PJVA to extract and label gesture data from both the MSRC- 12 and PJVA-20 datasets.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".