Gait Analysis with an Integrated Mobile Robot and Wearable Sensor System Reveals Associations Between Cognitive Ability and Dynamic Balance in Older Adults
Bibliographic record
Abstract
Gait abnormalities and postural instability have been linked to cognitive decline in older adults, however the causal relationships between cognitive capacity and gait is still an open problem. Emerging portable technologies may help elucidate these connections by enabling gait analysis in out-of-the-lab settings, with higher sensitivity than timed gait assessment tests. The purpose of this work was to evaluate the associations between cognitive ability (Montreal Cognitive As-sessment scores) and measures of gait and balance disturbance (spatiotemporal gait parameters, dynamic margin of stability) in a group of older adults, under a dual-task walking paradigm, using an integrated gait analysis system that features a mobile robot and in-shoe sensors. Results of hierarchical regression analyses adjusted for age and gender indicated that decline in cognitive ability in older adults is independently associated with more conservative overground gait patterns (i.e., smaller absolute values of the anteroposterior margin of stability) and increased gait variability (i.e., larger coefficients of variation in stride time and stride velocity) when performing dual-task walking. These results provide proof-of-concept validation of the applicability of integrated robotic and wearable sensors technologies to out-of-the-lab gait analysis in older adults.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".