Automated detection of handovers using kinematic features
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates the use of kinematic motions recognized by a support vector machine (SVM) for the automatic detection of object handovers from the perspective of an object receiver. The classifier uses the giver’s kinematic behaviors (e.g. joint angles, distances of joints from each other and with respect to the receiver) to determine a giver’s intent to hand over an object. We used a bagged random forest to determine how informative features were in predicting the occurrence of handovers, and to assist in selecting a core set of features to be used by the classifier. Altogether, 22 kinematic features were chosen for developing handover detection models and later testing of generalization performance. Test results indicated an overall maximum accuracy of 97.5% by the SVM in its capacity to distinguish between handover and non-handover motions. The classification ability of the SVM was found to be unaffected across four kernel functions (linear, quadratic, cubic and radial basis). These results demonstrate considerable potential for detection of handovers and other gestures for human–robot interaction using kinematic features.
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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 | 0.003 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.003 | 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