Differences in Trunk Accelerometry Between Frail and Nonfrail Elderly Persons in Sit-to-Stand and Stand-to-Sit Transitions Based on a Mobile Inertial Sensor
Bibliographic record
Abstract
BACKGROUND: Clinical frailty syndrome is a common geriatric syndrome, which is characterized by physiological reserve decreases and increased vulnerability. The changes associated to ageing and frailties are associated to changes in gait characteristics and the basic functional capacities. Traditional clinical evaluation of Sit-to-Stand (Si-St) and Stand-to-Sit (St-Si) transition is based on visual observation of joint angle motion to describe alterations in coordination and movement pattern. The latest generation smartphones often include inertial sensors with subunits such as accelerometers and gyroscopes, which can detect acceleration. OBJECTIVE: Firstly, to describe the variability of the accelerations, angular velocity, and displacement of the trunk during the Sit-to-Stand and Stand-to-Sit transitions in two groups of frail and physically active elderly persons, through instrumentation with the iPhone 4 smartphone. Secondly, we want to analyze the differences between the two study groups. METHODS: A cross-sectional study that involved 30 subjects over 65 years, 14 frail and 16 fit subjects. The participants were classified with frail syndrome by the Fried criteria. Linear acceleration was measured along three orthogonal axes using the iPhone 4 accelerometer. Each subject performed up to three successive Si-St and St-Si postural transitions using a standard chair with armrest. RESULTS: Significant differences were found between the two groups of frail and fit elderly persons in the accelerometry and angular displacement variables obtained in the kinematic readings of the trunk during both transitions. CONCLUSIONS: The inertial sensor fitted in the iPhone 4 is able to study and analyze the kinematics of the Si-St and St-Si transitions in frail and physically active elderly persons. The accelerometry values for the frail elderly are lower than for the physically active elderly, while variability in the readings for the frail elderly is also lower than for the control group.
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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".