Vibration for stimulating limb proprioceptors: Measurement, characteristics, and challenges
Bibliographic record
Abstract
Using tendon/muscle vibration to stimulate Ia afferents in rehabilitation research is increasing in popularity. Tendon vibration can also be used to stimulate the mechanoreceptors with the goal of attenuating proprioception. For therapeutic purposes, tendon vibration must be within known amplitude(~0.5mm) and frequency(80-120Hz) ranges. However, there is no standard and portable method established for measuring vibration characteristics. The aim of the current study was to describe the characteristics of the movements of a vibration motor and explore the feasibility of using an affordable accelerometer to measure vibration characteristics. Movements of a small vibration motor mounted on a participant's wrist were simultaneously measured using an Optotrak 3D Investigator and accelerometer. Five vibration intensities (55%,65%,75%,85%,100% of motor capacity) were measured for five 30-second trials each. The main outcome measures were frequency, displacement and peak acceleration of the vibration from the Optotrak and accelerometer. Pearson correlations showed a strong positive relationship between accelerometer and Optotrak measurements of vibration frequency for 55%,65%,75%,85%, and 100% vibration intensities (r=0.86,0.99,1.00,1.00,1.00, respectively). The maximum acceleration of the motor's movement ranged from ±42.5m.s^2 to ±149.0m.s^2 for different vibration intensities as measured by the Optotrak. This range of acceleration is above the measurement range of the accelerometer used (range ±3g). Thus, the measurements of the accelerometer for vibration amplitude could not be validated. The results of this study showed that affordable accelerometers are capable of measuring the frequency of the vibration with high precision. A follow-up study will explore the validity of vibration amplitude measurement using an accelerometer with a measurement range of ~±10g.
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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 | 0.004 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".