Tools and Techniques Used With Robotic Devices to Quantify Upper-Limb Function in Typically Developing Children: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Robotic devices have been used to quantify function, identify impairment, and rehabilitate motor function extensively in adults, but less-so in younger populations. The ability to perform motor actions improves as children grow. It is important to quantify this rate of change of the neurotypical population before attempting to identify impairment and target rehabilitation techniques. OBJECTIVES: For a population of typically developing children, this systematic review identifies and analyzes tools and techniques used with robotic devices to quantify upper-limb motor function. Since most of the papers also used robotic devices to compare function of neurotypical to pathological populations, a secondary objective was introduced to relate clinical outcome measures to identified robotic tools and techniques. METHODS: Five databases were searched between February 2019 and August 2020, and 226 articles were found, 19 of which are included in the review. RESULTS: Robotic devices, tasks, outcome measures, and clinical assessments were not consistent among studies from different settings but were consistent within laboratory groups. Fifteen of the 19 articles evaluated both typically developing and pathological populations. CONCLUSION: To optimize universally comparable outcomes in future work, it is recommended that a standard set of tasks and measures is used to assess upper-limb motor function. Standardized tasks and measures will facilitate effective rehabilitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".