Prototype of an Advanced Ankle Proprioceptive Acuity Assessment Device
Bibliographic record
Abstract
Body movement and balance are critical for daily activities; compromised balance leads to falls, especially among the elderly and those with neurological disorders like Parkinson's disease.Falls, a primary cause of unintentional injuries in older adults, are twice as common in Parkinson's patients.Many falls stem from impaired postural balance, often linked to compromised proprioception, heightening fall risk and fostering social isolation.Understanding sensory mechanisms is vital for effective fall prevention.A novel device, with two active platforms controlled by a microcontroller, assesses ankle proprioceptive acuity and tests postural perturbations, aiding in fall prevention strategies.The ankle proprioceptive acuity device boasts two independent active platforms with individual control, allowing concurrent assessment of ankle proprioception for both feet.To validate its functionality, we conducted kinesthesia assessments on six healthy individuals and two old people (Age >60).This measure serves as a key indicator of ankle proprioceptive acuity, ensuring the efficacy of the device's control system.The results of the kinesthesia assessments indicate that, on average, healthy individuals recorded a mean sense of motion score of 1.7 degrees per second with a standard deviation of 0.4, while older recorded a mean score of 7.2 degrees per second with a standard deviation of 0.7.These preliminary findings provide valuable insights into the device's functionality but are not statistically significant due to the small sample size.Nonetheless, these results lay the groundwork for future research.Moving forward, the device can be utilized to evaluate proprioception in both sitting and standing positions and introduce disturbances to further investigate balance and stability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".