Artificial Neural Networks and Center-of-Pressure Modeling: A Practical Method for Sensorimotor-Degradation Assessment
Bibliographic record
Abstract
Numerous methods for studying the prevention of falls and age-related sensorimotor degradation have been proposed and tested. Some approaches are too impractical to use with seniors or too expensive for practitioners. Practitioners desire a simple, reliable technique. The goals of this research were to develop such an approach and to apply it in exploring the effect of Tai Chi on age-related sensorimotor degradation. The method employed artificial-neural-network (ANN) models trained by using individuals' center-of-pressure (COP) measurements and age. Ninety-six White and Chinese adults without Tai Chi training were tested. In contrast, a third group, Chinese seniors with Tai Chi training, was tested to ascertain any influence from Tai Chi on sensorimotor aging. This study supported ANN technology with COP data as a feasible tool in the exploration of sensorimotor degradation and demonstrated that Tai Chi slowed down the effects of sensorimotor aging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".