Biomechanics of reaching: clinical implications for individuals with acquired brain injury
Bibliographic record
Abstract
PURPOSE: Outline the biomechanics of reaching both in healthy individuals and in individuals with acquired brain injury (ABI), and to discuss the clinical implications for using valid biomechanical models to assess reaching. METHODS: A review of current literature, including a MEDLINE search using keywords of reaching, ABI, stroke, biomechanics and motor control. RESULTS: Current assessments of the upper extremity in ABI are focused on single-joint characteristics of range of motion, strength and spasticity. However, reaching is a functional multijoint task requiring interjoint coordination in addition to feedback and feedforward control to position the hand optimally at a desired location so that it may interact with the environment. From the literature, biomechanical measures of reaching such as movement time, movement distance and interjoint coordination have been shown to discriminate changes to hand path quality following brain injury. These measures have also been shown to correlate with measures of sensorimotor function (e.g. Fugl-Meyer) in the upper extremity. CONCLUSIONS: Further development of reliable and valid multi-joint biomechanical evaluations is required, particularly for natural and goal-oriented reaching movements. The biomechanical assessment of reaching in ABI can provide an understanding of the specific deficits in physiological structures or motor planning underlying altered reaching ability, assist in the evaluation of new therapies, and characterize the recovery process following ABI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".