Arm–Trunk Coordination for Beyond-the-Reach Movements in Adults With Stroke
Bibliographic record
Abstract
BACKGROUND: By involving additional degrees of freedom, the nervous system may preserve hand trajectories when making pointing movements with or without trunk displacement. Previous studies indicate that the potential contribution of trunk movement to hand displacement for movements made within arm reach is neutralized by appropriate compensatory shoulder and elbow rotations. For beyond-the-reach movements, compensatory coordination is attenuated after the hand peak velocity, allowing trunk movement to contribute to hand displacement. OBJECTIVE: To investigate if the timing and spatial coordination of arm and trunk movements during beyond-the-reach movements is preserved in stroke. METHODS: Eleven healthy control subjects and 11 individuals with mild-to-moderate chronic unilateral hemiparesis participated. Arm and trunk kinematics during 60 target reaches to an ipsilaterally placed target were recorded. In 30% of randomly chosen trials, trunk movement was unexpectedly prevented (blocked-trunk trials) by an electromagnetic device, resulting in divergence of the hand trajectory from that in free-trunk trials. Hand trajectories and elbow-shoulder interjoint coordination were compared between trials. RESULTS: In stroke participants, hand trajectory divergence occurred at a shorter movement extent and interjoint coordination patterns diverged at a relatively greater distance compared to controls. Thus, arm movements in stroke participants only partially compensated trunk displacement resulting in the trunk movement contributing to arm movement earlier and to a larger extent during reaching. CONCLUSION: Individuals with mild-to-moderate stroke have deficits in timing and spatial coordination of arm and trunk movements during different parts of a reaching movement. This deficit may be targeted in therapy to improve upper limb function.
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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 | 0.000 | 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) | 0.002 | 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".