Evaluating the Usability of Inertial Measurement Units for Measuring and Monitoring Activity Post-Stroke: A Scoping Review
Bibliographic record
Abstract
Stroke is the most common cause of disability in adults, resulting in declines in overall activity. Inertial measurement units (IMUs) allow for the monitoring of activity patterns in various settings, informing clinical interventions and patient self-management. This review aimed to synthesize existing research evaluating the usability of IMUs for monitoring activity in people with stroke. This scoping review was conducted according to PRISMA guidelines. The MEDLINE, Embase, and CINAHL databases were searched for publications evaluating the usability of IMUs for monitoring activity post-stroke. Fourteen publications met the inclusion criteria. Most studies were conducted in chronic stroke with data collected in real-world conditions. Usability findings indicated that most stroke participants and clinicians reported a positive user experience; however many reported difficulties with devices due to stroke-related impairments. Importantly, the interpretation of this finding was impacted by poorly reported impairments of stroke participants. Only a few studies evaluated end-user experiences (people with stroke and clinicians) in interpreting and engaging with feedback based on data from IMUs. Future usability studies in stroke populations should aim to understand participant characteristics that influence device engagement, with a focus on user experience in interpreting device-generated metrics.
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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 | 0.015 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".