“Stepping Up” Activity Poststroke: Ankle-Positioned Accelerometer Can Accurately Record Steps During Slow Walking
Bibliographic record
Abstract
BACKGROUND: As physical activity in people poststroke is low, devices that monitor and provide feedback of walking activity provide motivation to engage in exercise and may assist rehabilitation professionals in auditing walking activity. However, most feedback devices are not accurate at slow walking speeds. OBJECTIVE: This study assessed the accuracy of one accelerometer to measure walking steps of community-dwelling individuals poststroke. DESIGN: This was a cross-sectional study. METHODS: Two accelerometers were positioned on the nonparetic waist and ankle of participants (N=43), and walking steps from these devices were recorded at 7 speeds (0.3-0.9 m/s) and compared with video recordings (gold standard). RESULTS: When positioned at the waist, the accelerometer had more than 10% error at all speeds, except 0.8 and 0.9 m/s, and numerous participants recorded zero steps at 0.3 to 0.5 m/s. The device had 10% or less error when positioned at the ankle for all speeds between 0.4 and 0.9 m/s. LIMITATIONS: Some participants were unable to complete the faster walking speeds due to their walking impairments and inability to maintain the requested walking speed. CONCLUSIONS: Although not recommended by the manufacturer, positioning the accelerometer at the ankle (compared with the waist) may fill a long-standing need for a readily available device that provides accurate feedback for the altered and slow walking patterns that occur with stroke.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".