Eyewear Equipped with a Triaxial Accelerometer Detects Age-Related Changes in Ambulatory Activity
Bibliographic record
Abstract
Aging is known as a risk factor for gait disorders, which lead to reduced quality of life. Gait disorders can potentially be a sign of a preclinical phase of neurological diseases. Therefore, routine monitoring of changes in ambulatory activity with age can lead to early detection of such disorders. JINS MEME is eyewear equipped with a triaxial accelerometer (mediolateral, anteroposterior, and vertical) and capable of measuring acceleration signals during gait. To validate effectiveness of JINS MEME in routinely monitoring age-related changes in ambulatory activity, the present study tested three hypotheses: (1) the frequency of mediolateral body sway during gait increases with age, (2) the variability of gait speed (anteroposterior) increases with age, and (3) the frequency of vertical body sway during gait increases with age. The present study included 118 subjects aged 25–69 years. The acceleration signals were measured by JINS MEME while each subject walked down a barrier-free 20-meter-long level corridor at a natural pace. Triaxial variances known for reflecting gait stability, were calculated from the acceleration signals during gait. An association between each of the triaxial variances and age was assessed by multiple linear robust regression analysis including sex as a nuisance covariate. We found significant positive correlations between the anteroposterior variance and age and between the vertical variance and age. The results supported our second and third hypotheses and raised an intriguing possibility that the triaxial accelerometer of JINS MEME is capable of detecting age-related changes in ambulatory activity.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".