Pupillometry for Arm and Hand Motor Intent Detection
Bibliographic record
Abstract
Rehabilitation robots and assistive devices that detect the motor intent of their users can provide more intuitive and effective control. Pupil dilation occurs when people perform motor activities, but its utility for detecting motor intent has not been explored previously. In this work, a human participant research study is conducted to determine if pupillometric data can be used to differentiate between a person's intent to pick up or observe an object. Thirty participants were recruited to perform 120 trials of picking up and observing objects while their pupil dilation was recorded by an eye tracking headset. Features were extracted from the time series data and used to train a neural network classifier. The classifier was tested using leave-one-out cross-validation. The classifier achieved an average accuracy of 59.4% and F1 score of 0.578 across the thirty test datasets. The performance varied significantly depending on the participant used for testing, suggesting that the pupillometric approach to intent detection may be better suited to some participants than others. Future work should determine whether intent detection can be improved with more advanced machine learning methods, such as convolutional neural networks (CNN), and whether intent detection can be performed in real time.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".