Preliminary Examination of the Ability of a New Wearable Device to Capture Functional Hand Activity After Stroke
Bibliographic record
Abstract
Background and Purpose- A reliable measure of movement repetitions is required to assist in determining the optimal dose for maximizing upper limb recovery after stroke. This study investigated the ability of a new wearable device to capture reach-to-grasp repetitions in individuals with stroke. Methods- Eight individuals with stroke wore an instrumented wrist bracelet while completing 12 upper limb activities. Participants completed 5 and 10 repetitions of each activity on 2 separate sessions (time 1 and time 2) and completed clinical assessments (Fugl-Meyer Upper Extremity Assessment and Action Research Arm Test). Mean reach-to-grasp counts (ie, hand counts) were compared across activities. Scaling properties were assessed by the ratio of 10 repetitions to 5 repetitions for the activities (ie, expected value of 2). Bland-Altman diagrams were used to examine agreement between time 1 and time 2 counts. Results- The wrist bracelet averaged 0 to 0.6 hand counts per repetition for the arm-only and hand-only activities and averaged 1 to 2 counts per repetition of the reach-to-grasp activities. The mean ratio of 10 repetition to 5 repetition counts was ≈2 for all of the reach-to-grasp activities. Mean differences from time 1 to time 2 were <0.3 counts/repetition for all activities except one. Conclusions- These preliminary results provide evidence that the wrist bracelet is able to capture hand counts over a variety of tasks in a consistent manner. This wrist bracelet could be further developed as a tool to record dose of upper limb practice for research or clinical practice, as well as providing motivation and accountability to patients participating in treatments requiring upper limb movement repetitions. Currently, there are limitations in interpreting the impact of impairment and common compensatory movements on hand counts, and it would be valuable for future studies to explore these effects.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".