Enabling gestural interaction by means of tracking dynamical systems models and assistive feedback
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
The computational understanding of continuous human movement plays a significant role in diverse emergent applications in areas ranging from human computer interaction to physical and neuro-rehabilitation. Non-visual feedback can aid the continuous motion control tasks that such applications frequently entail. An architecture is introduced for enabling interaction with a system that furnishes a number of gestural affordances with assistive feedback. The approach combines machine learning techniques for understanding a user's gestures with a method for the display of salient features of the underlying inference process in real time. Methods used include a particle filter to track multiple hypotheses about a user's input as the latter is unfolding, together with models of the nonlinear dynamics intrinsic to the movements of interest. Non-visual feedback in this system is based on a presentation of error features derived from an estimate of the sampled time varying probability that the user's gesture corresponds to the various tracked state trajectories in the different dynamical systems. We describe applications to interactive systems for human gait analysis and rehabilitation, a domain of considerable current interest in the movement sciences and health care.
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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 | 0.001 | 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 | 0.001 |
| 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