Pattern Recognition and Characterization of Upper Limb Neuromuscular Dynamics during Driver-Vehicle Interactions
Bibliographic record
Abstract
In this work, pattern recognition and characterization of the neuromuscular dynamics of driver upper limb during naturalistic driving were studied. During the human-in-the-loop experiments, two steering tasks, namely, the passive and active steering tasks, were instructed to be completed by the subjects. Furthermore, subjects manipulated the steering wheel with two distinct postures and six different hand positions. The neuromuscular dynamics of subjects' upper limb were measured using electromyogram signals, and the behavioral data, including the steering torque and steering angle, were also collected. Based on the experimental data, patterns of muscle activities during naturalistic driving were investigated. The correlations, amplitudes, and responsiveness of the electromyogram signals, as well as the smoothness and regularity of the steering torque were discussed. The results reveal the mechanisms of neuromuscular dynamics of driver upper limb and provide a theoretical foundation for the design of the future human-machine interface for automated vehicles.
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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 | 0.000 | 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) | 0.002 | 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".