Real-World Functional Grasping Activity in Individuals With Stroke and Healthy Controls Using a Novel Wearable Wrist Sensor
Bibliographic record
Abstract
Background. While wrist-worn accelerometers have been used to measure upper extremity use in the past, they primarily measure arm motion and lack the ability to capture functional hand opening and grasping activities which are essential for activities of daily living. Objectives. To characterize real-world functional hand opening and grasping activities captured over multiple days in adults with stroke and in matched controls using a novel wrist-worn device. Methods. Twenty-eight individuals (fourteen individuals with stroke and 14 healthy controls) wore the devices on both wrists for 3 days. Functional hand activity was characterized by daily hand counts, hourly hand counts, and asymmetry between hands. The Mann–Whitney U test was used to evaluate differences in functional hand activities between the two groups. Results. The stroke group had 1480 and 4691 daily hand counts in their affected and nonaffected hands, respectively. The control group had 3559 and 5021 daily hand counts in their nondominant and dominant hands, respectively. Significantly fewer daily hand counts (P = .019), fewer hourly hand counts (P = .024), and a larger asymmetry index (P = .01) of the affected hand in the stroke group were found compared to that of the nondominant hand in the control group. Conclusions. Real-world functional upper extremity activity can be measured using this novel wrist-worn device. Unlike wrist-worn accelerometers, this wrist-worn device can provide a measurement of functional grasping activity. The findings have implications for clinicians and researchers to monitor and assess real-world hand activity, as well as to apply specific doses of repetitions to improve neural recovery after 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 | 0.000 | 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".