A Wearable System for Experimental Knee Pain During Real-World Locomotion: Habituation and Motor Adaptation
Bibliographic record
Abstract
We developed a novel, wearable system that couples motion sensing and electrical stimulation in real-time to study motor adaptation in new environments. In two experiments we established key information needed in the development of our system including 1) pain habituation patterns and motor adaptations to knee pain while walking; 2) a model of electrical stimulation magnitude as a function of pain perception; and 3) gait-phase-dependent modulation of pain intensity. Over three 10-minute walking bouts, we observed significant pain habituation (p1/10, though a piecewise linear (Adj R$^{{2}} =0.874$) or exponential model (Adj R$^{{2}} =0.869$) was required to fit the perception data across the stimulus intensity range (0-5/10). Finally, participants did not report gait-phase-dependent modulation of pain intensity while walking with tonic electrical stimulation. Our wearable system supports new motor adaptation experiments in novel contexts not previously possible. These results show the system induces localized pain perceptions and motor adaptations in complex movements (walking) while providing guidelines to structure future experimental pain studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".