Enhancing safety monitoring in post-stroke rehabilitation through wearable technologies
Bibliographic record
Abstract
Objective Current clinical practice guidelines support structured, progressive protocols for improving walking after stroke. Technology enables monitoring of exercise and therapy intensity, but safety concerns could also be addressed. This study explores functional mobility in post-stroke individuals using wearable technology to quantify movement smoothness—an indicator of safe mobility. Design Observational cohort study. Setting A movement analysis and rehabilitation laboratory. Participants A total of 56 chronic post-stroke individuals and 51 healthy controls. Intervention Participants performed the mobility test while wearing an inertial measurement unit attached to their waist. Thirty-two healthy participants also engaged in a steady-state walking task. Main measures Functional mobility smoothness by examining angular velocities in the yaw, pitch, and roll axes, employing the spectral arc length metrics. Results Our findings reveal that post-stroke individuals extend the duration of the timed-up-and-go test (≈9 s and 23 s longer compared to the controls) to ensure safe mobility—greater mobility smoothness ( p < 0.001). Notably, for mild and severe impairments, post-stroke mobility demonstrated ≈8% and ≈11% greater smoothness in pitch movements, respectively ( p = 0.025 and p = 0.002). In the roll direction, mobility was ≈12% smoother in cases of severe strokes ( p = 0.006). Conclusion This study addresses a crucial gap in the understanding of mobility smoothness in chronic stroke survivors using wearable technology. Our study suggests the potential utility of spectral arc length to predict challenging mobility situations in real-world situations. We highlight the potential for automated monitoring of safety offering promising avenues for real-time, real-life monitoring.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".