Quantifying Gait and Posture in Geriatric Inpatients Using Inertial Sensors and Posturography: A Cross-Sectional Study
Bibliographic record
Abstract
Background/Objectives: Mobility screening is standard practice in hospitalized geriatric patients, but clinical assessments alone may not fully capture functional capacity and related risks. This study aimed to describe the physical performance (gait analysis, postural stability and regulation) and clinical–functional status (e.g., Tinetti [TIN], Barthel Index [BI]) in geriatric inpatients, and to explore associations between measures from different domains. Methods: Fifty-five geriatric inpatients (mean age: 84.3 ± 5.47 years, range: 71–97; 49% female) underwent spatiotemporal gait analysis (inertial sensor system/RehaGait) and posturography (Interactive Balance System). Clinical assessments included TIN, BI, Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), Clinical Frailty Scale (CFS), and Numeric Rating Scale (NRS). Gait and postural data were compared with age-, sex-, and height-adjusted reference values. Results: Clinical data indicated a low fall risk (TIN: 24), moderate functional independence (BI: 54), and moderate frailty (CFS: 5). Deviations from reference values were more frequent in gait parameters (18/50%) than in postural parameters (6/17%), with postural stability consistently reduced. The largest differences for the geriatric patients compared with the reference gait data were found for stride length, walking speed, double and single support, roll-off angle, and landing angle. TIN showed the strongest correlation with walking speed (r = 0.47, 95% CI: 0.22–0.67), a relationship unaffected by gender (partial r = 0.52). Conclusions: Gait assessment revealed greater performance deficits than postural measures in this cohort.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".