A NOVEL MEASUREMENT SYSTEM FOR QUANTITATIVE ASSESSMENT OF AGE-RELATED SENSORI-MOTOR DEGRADATION
Bibliographic record
Abstract
Early identification of individuals with impaired balancing ability could lead to timely interventions and reduce the hazard of age-related falls. Numerous methods for researching the prevention of falls and age-related sensori-motor degradation have been proposed and tested. Most are either too expensive for practitioners or too physically demanding to use with seniors. A simple, reliable technique is desired. The aim of this research is to develop a practical and quantitative solution for assessment of age-related degradation of human sensori-motor function, which could in turn serve as a means of fall prevention among seniors. A novel testing apparatus, the dynamic balance testing platform, was developed. The design includes artificial neural network (ANN) technology to address the nonlinearity and redundancy in the neural network that controls sensori-motor functions. A total of 62 male subjects aged from 18 to 84 years were tested using the proposed method. Results showed that (1) the new device did reflect the sensori-motor degradation related to age, (2) reliable evaluation of sensori-motor function need not be complicated, time consuming, or costly, and (3) the developed equipment powered with ANN technology holds great potentials for predicting fall possibility. Overall, this study validated a strategy of fall prevention with a potential for prevalent use in the healthcare industry.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".